An Heterogeneous Population-Based Genetic Algorithm for Data Clusteringijeei-iaes
As a primary data mining method for knowledge discovery, clustering is a technique of classifying a dataset into groups of similar objects. The most popular method for data clustering K-means suffers from the drawbacks of requiring the number of clusters and their initial centers, which should be provided by the user. In the literature, several methods have proposed in a form of k-means variants, genetic algorithms, or combinations between them for calculating the number of clusters and finding proper clusters centers. However, none of these solutions has provided satisfactory results and determining the number of clusters and the initial centers are still the main challenge in clustering processes. In this paper we present an approach to automatically generate such parameters to achieve optimal clusters using a modified genetic algorithm operating on varied individual structures and using a new crossover operator. Experimental results show that our modified genetic algorithm is a better efficient alternative to the existing approaches.
Artificial Bee Colony Based Multiview Clustering ABC MVC for Graph Structure ...ijtsrd
Combining data from several information sources has become a significant research area in classification by several scientific applications. Many of the recent work make use of kernels or graphs in order to combine varied categories of features, which normally presume one weight for one category of features. These algorithms don't consider the correlation of graph structure between multiple views, and the clustering results highly based on the value of predefined affinity graphs. Artificial Bee Colony is combined to Multi view Clustering ABC MVC model in order to combine each and every one of features and learn the weight for each feature with respect to each cluster separately by new joint structured sparsity inducing norms. It also solves the issue of MVC by seamlessly combining the graph structures of varied views in order to completely make use of the geometric property of underlying data structure. ABC MVC model is based on the presumption with the purpose of intrinsic underlying graph structure would assign related connected part in each graph toward the similar group. Implementation results shows that the proposed ABC MVC model gets improved clustering accuracy than the other conventional methods such as Graph Structure Fusion GSF and Multiview Clustering with Graph Learning MVGL . The results are implemented to Caltech 101 and Columbia Object Image Library COIL 20 with respect to clustering accuracy ACC , Normalized Mutual Information NMI , and Adjusted Rand Index ARI N. Kamalraj ""Artificial Bee Colony Based Multiview Clustering (ABC-MVC) for Graph Structure Fusion in Benchmark Datasets"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30170.pdf
Paper Url : https://www.ijtsrd.com/computer-science/data-miining/30170/artificial-bee-colony-based-multiview-clustering-abc-mvc-for-graph-structure-fusion-in-benchmark-datasets/n-kamalraj
A survey on methods and applications of meta-learning with GNNsShreya Goyal
This survey paper has provided a comprehensive review of works that are a combination of graph neural networks (GNNs) and meta-learning. They have also provided a thorough review, summary of methods, and applications in these categories. The application of meta-learning to GNNs is a growing and exciting field; many graph problems will benefit immensely from the combination of the two approaches.
ACCOST is a method for differential analysis of Hi-C data between two conditions with replicates. It models Hi-C interaction counts with a negative binomial distribution that accounts for distance effects between loci through an offset term. ACCOST normalizes counts with ICE and estimates model parameters to obtain a p-value for each bin pair comparing the two conditions. It was validated on several datasets and shown to identify more differential contacts than other methods like diffHic and FIND, particularly at short genomic distances.
In order to solve the complex decision-making problems, there are many approaches and systems based on the fuzzy theory were proposed. In 1998, Smarandache introduced the concept of single-valued neutrosophic set as a complete development of fuzzy theory. In this paper, we research on the distance measure between single-valued neutrosophic sets based on the H-max measure of Ngan et al. [8]. The proposed measure is also a distance measure between picture fuzzy sets which was introduced by Cuong in 2013 [15]. Based on the proposed measure, an Adaptive Neuro Picture Fuzzy Inference System (ANPFIS) is built and applied to the decision making for the link states in interconnection networks. In experimental evaluation on the real datasets taken from the UPV (Universitat Politècnica de València) university, the performance of the proposed model is better than that of the related fuzzy methods.
IRJET- Evaluation of Classification Algorithms with Solutions to Class Imbala...IRJET Journal
This document discusses evaluating various classification algorithms to address class imbalance problems using the bank marketing dataset in WEKA. It first introduces data mining and classification algorithms like decision trees, naive Bayes, neural networks, support vector machines, logistic regression and random forests. It then discusses the class imbalance problem that occurs when one class is underrepresented. To address this, it explores sampling techniques like random under-sampling of the majority class, random over-sampling of the minority class, and SMOTE. It uses these techniques on the bank marketing dataset to evaluate the algorithms based on metrics like precision, recall, F1-score, ROC and AUCPR for the minority class.
This document discusses a model for analyzing how network connectivity impacts asset returns and risks. The model augments a traditional multi-factor model to account for systemic links between assets represented by a network connectivity matrix. The model shows that network links inflate asset loadings to common factors, impacting expected returns and total risk decomposition into systematic and idiosyncratic components. Greater network connectivity reduces diversification benefits by slowing the decrease in portfolio idiosyncratic risk as the number of assets increases. The authors propose extending the model to incorporate heterogeneous asset responses to links and time-varying network structures.
La statistique et le machine learning pour l'intégration de données de la bio...tuxette
This document summarizes a presentation on using statistics and machine learning for integrating high-throughput biological data. It discusses how biological data is large in volume, multi-scaled and heterogeneous in type, creating bottlenecks for analysis. It presents different methods for integrating multiple data tables, including multiple kernel learning to combine similarity matrices. An example application to TARA Oceans data is described, identifying Rhizaria abundance as structuring ocean differences. Interpretability of results is discussed along with prospects for deep learning and predicting phenotypes while understanding relationships.
An Heterogeneous Population-Based Genetic Algorithm for Data Clusteringijeei-iaes
As a primary data mining method for knowledge discovery, clustering is a technique of classifying a dataset into groups of similar objects. The most popular method for data clustering K-means suffers from the drawbacks of requiring the number of clusters and their initial centers, which should be provided by the user. In the literature, several methods have proposed in a form of k-means variants, genetic algorithms, or combinations between them for calculating the number of clusters and finding proper clusters centers. However, none of these solutions has provided satisfactory results and determining the number of clusters and the initial centers are still the main challenge in clustering processes. In this paper we present an approach to automatically generate such parameters to achieve optimal clusters using a modified genetic algorithm operating on varied individual structures and using a new crossover operator. Experimental results show that our modified genetic algorithm is a better efficient alternative to the existing approaches.
Artificial Bee Colony Based Multiview Clustering ABC MVC for Graph Structure ...ijtsrd
Combining data from several information sources has become a significant research area in classification by several scientific applications. Many of the recent work make use of kernels or graphs in order to combine varied categories of features, which normally presume one weight for one category of features. These algorithms don't consider the correlation of graph structure between multiple views, and the clustering results highly based on the value of predefined affinity graphs. Artificial Bee Colony is combined to Multi view Clustering ABC MVC model in order to combine each and every one of features and learn the weight for each feature with respect to each cluster separately by new joint structured sparsity inducing norms. It also solves the issue of MVC by seamlessly combining the graph structures of varied views in order to completely make use of the geometric property of underlying data structure. ABC MVC model is based on the presumption with the purpose of intrinsic underlying graph structure would assign related connected part in each graph toward the similar group. Implementation results shows that the proposed ABC MVC model gets improved clustering accuracy than the other conventional methods such as Graph Structure Fusion GSF and Multiview Clustering with Graph Learning MVGL . The results are implemented to Caltech 101 and Columbia Object Image Library COIL 20 with respect to clustering accuracy ACC , Normalized Mutual Information NMI , and Adjusted Rand Index ARI N. Kamalraj ""Artificial Bee Colony Based Multiview Clustering (ABC-MVC) for Graph Structure Fusion in Benchmark Datasets"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30170.pdf
Paper Url : https://www.ijtsrd.com/computer-science/data-miining/30170/artificial-bee-colony-based-multiview-clustering-abc-mvc-for-graph-structure-fusion-in-benchmark-datasets/n-kamalraj
A survey on methods and applications of meta-learning with GNNsShreya Goyal
This survey paper has provided a comprehensive review of works that are a combination of graph neural networks (GNNs) and meta-learning. They have also provided a thorough review, summary of methods, and applications in these categories. The application of meta-learning to GNNs is a growing and exciting field; many graph problems will benefit immensely from the combination of the two approaches.
ACCOST is a method for differential analysis of Hi-C data between two conditions with replicates. It models Hi-C interaction counts with a negative binomial distribution that accounts for distance effects between loci through an offset term. ACCOST normalizes counts with ICE and estimates model parameters to obtain a p-value for each bin pair comparing the two conditions. It was validated on several datasets and shown to identify more differential contacts than other methods like diffHic and FIND, particularly at short genomic distances.
In order to solve the complex decision-making problems, there are many approaches and systems based on the fuzzy theory were proposed. In 1998, Smarandache introduced the concept of single-valued neutrosophic set as a complete development of fuzzy theory. In this paper, we research on the distance measure between single-valued neutrosophic sets based on the H-max measure of Ngan et al. [8]. The proposed measure is also a distance measure between picture fuzzy sets which was introduced by Cuong in 2013 [15]. Based on the proposed measure, an Adaptive Neuro Picture Fuzzy Inference System (ANPFIS) is built and applied to the decision making for the link states in interconnection networks. In experimental evaluation on the real datasets taken from the UPV (Universitat Politècnica de València) university, the performance of the proposed model is better than that of the related fuzzy methods.
IRJET- Evaluation of Classification Algorithms with Solutions to Class Imbala...IRJET Journal
This document discusses evaluating various classification algorithms to address class imbalance problems using the bank marketing dataset in WEKA. It first introduces data mining and classification algorithms like decision trees, naive Bayes, neural networks, support vector machines, logistic regression and random forests. It then discusses the class imbalance problem that occurs when one class is underrepresented. To address this, it explores sampling techniques like random under-sampling of the majority class, random over-sampling of the minority class, and SMOTE. It uses these techniques on the bank marketing dataset to evaluate the algorithms based on metrics like precision, recall, F1-score, ROC and AUCPR for the minority class.
This document discusses a model for analyzing how network connectivity impacts asset returns and risks. The model augments a traditional multi-factor model to account for systemic links between assets represented by a network connectivity matrix. The model shows that network links inflate asset loadings to common factors, impacting expected returns and total risk decomposition into systematic and idiosyncratic components. Greater network connectivity reduces diversification benefits by slowing the decrease in portfolio idiosyncratic risk as the number of assets increases. The authors propose extending the model to incorporate heterogeneous asset responses to links and time-varying network structures.
La statistique et le machine learning pour l'intégration de données de la bio...tuxette
This document summarizes a presentation on using statistics and machine learning for integrating high-throughput biological data. It discusses how biological data is large in volume, multi-scaled and heterogeneous in type, creating bottlenecks for analysis. It presents different methods for integrating multiple data tables, including multiple kernel learning to combine similarity matrices. An example application to TARA Oceans data is described, identifying Rhizaria abundance as structuring ocean differences. Interpretability of results is discussed along with prospects for deep learning and predicting phenotypes while understanding relationships.
Opinion mining framework using proposed RB-bayes model for text classicationIJECEIAES
Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.
A preliminary survey on optimized multiobjective metaheuristic methods for da...ijcsit
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
This document discusses probabilistic modeling and decision theory. It describes how probabilistic models can be used to make decisions based on observed data and prior knowledge. The key aspects covered include:
- Probabilistic models express the probability of outcomes given observed data.
- Decision theory involves assigning costs to outcomes and choosing the action that maximizes expected utility.
- Common probabilistic models for decision making include Bayesian networks, neural networks, and support vector machines.
- Bayesian networks represent conditional dependencies graphically and can be used for inference, parameter estimation, and structure learning.
ADABOOST ENSEMBLE WITH SIMPLE GENETIC ALGORITHM FOR STUDENT PREDICTION MODELijcsit
Predicting the student performance is a great concern to the higher education managements.This
prediction helps to identify and to improve students' performance.Several factors may improve this
performance.In the present study, we employ the data mining processes, particularly classification, to
enhance the quality of the higher educational system. Recently, a new direction is used for the improvement
of the classification accuracy by combining classifiers.In thispaper, we design and evaluate a fastlearning
algorithm using AdaBoost ensemble with a simple genetic algorithmcalled “Ada-GA” where the genetic
algorithm is demonstrated to successfully improve the accuracy of the combined classifier performance.
The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,
especially in very large classes. This early prediction allows the instructor to provide appropriate advising
to those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the results
showed that this algorithm hassuccessfully improved the detection accuracy as well as it reduces the
complexity of computation.
This document provides an overview of a survey of multi-objective evolutionary algorithms for data mining tasks. It discusses key concepts in multi-objective optimization and evolutionary algorithms. It also reviews common data mining tasks like feature selection, classification, clustering, and association rule mining that are often formulated as multi-objective problems and solved using multi-objective evolutionary algorithms. The survey focuses on reviewing applications of multi-objective evolutionary algorithms for feature selection and classification in part 1, and applications for clustering, association rule mining and other tasks in part 2.
Discussion of “Network Connectivity and Systematic Risk” and “The Impact of N...SYRTO Project
Discussion of “Network Connectivity and Systematic Risk” and “The Impact of Network Connectivity on Factor Exposures, Asset pricing and Portfolio Diversification” by Billio, Caporin, Panzica and Pelizzon. Arjen Siegmann. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
Systemic and Systematic risk - Monica Billio, Massimiliano Caporin, Roberto Panzica, Loriana Pelizzon
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
A SYSTEM OF SERIAL COMPUTATION FOR CLASSIFIED RULES PREDICTION IN NONREGULAR ...ijaia
Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models,
our previous research work has developed a system of a regular ontology that models learning structures
in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has
led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed
for inductive learning processes and decision making in a multiagent system. But not all processes or
models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict
the required number of rules of a non-regular ontology model given some defined parameters.
This document discusses challenges in comparing structured models using cross-validation. It presents a decision-theoretic framework for model assessment and selection based on expected predictive loss. Different methods for estimating predictive loss are discussed, including k-fold cross-validation which is used to estimate the predictive loss of various multilevel models for a dataset from the Cooperative Congressional Election Survey with deeply nested demographic variables.
Improved probabilistic distance based locality preserving projections method ...IJECEIAES
In this paper, a dimensionality reduction is achieved in large datasets using the proposed distance based Non-integer Matrix Factorization (NMF) technique, which is intended to solve the data dimensionality problem. Here, NMF and distance measurement aim to resolve the non-orthogonality problem due to increased dataset dimensionality. It initially partitions the datasets, organizes them into a defined geometric structure and it avoids capturing the dataset structure through a distance based similarity measurement. The proposed method is designed to fit the dynamic datasets and it includes the intrinsic structure using data geometry. Therefore, the complexity of data is further avoided using an Improved Distance based Locality Preserving Projection. The proposed method is evaluated against existing methods in terms of accuracy, average accuracy, mutual information and average mutual information.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
The document describes a method for tracking objects of deformable shapes in images. It proposes representing the matching of a deformable template to an image as a minimum cost cyclic path in a product space of the template and image. An energy functional is introduced that consists of a data term favoring strong image gradients, a shape consistency term favoring similar tangent angles, and an elastic penalty. Optimization is performed using a minimum ratio cycle algorithm parallelized on GPUs. This provides efficient, pixel-accurate segmentation and correspondence between template and image curve. The method can be extended to 4D to segment and track multiple deformable anatomical structures in medical images.
This document presents a novel (R, S)-norm entropy measure to quantify the degree of fuzziness in intuitionistic fuzzy sets (IFSs). The entropy measure is defined based on (R, S)-norms and is proven to satisfy valid properties of an entropy measure. The entropy measure is then used to propose two decision-making approaches for multi-attribute decision making problems where attribute weights are either partially known or completely unknown. An example is provided to illustrate the decision making process using the novel entropy measure.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document discusses distance similarity measures that can be used for data mining classification and clustering techniques. It proposes a novel distance similarity measure called "Supervised & Unsupervised learning" that uses Euclidean distance similarity to partition training data into clusters. It then builds decision trees on each cluster to improve classification performance. The document also discusses using these measures for other applications like image processing, where k-means clustering can be used to segment images into clusters of similar pixel intensities. In conclusion, it states these similarity measures can help analyze complex datasets for business analysis purposes.
The document proposes a methodology to improve evolutionary multi-objective algorithms (EMOAs) by incorporating achievement scalarizing functions (ASFs) to provide convergence to the Pareto optimal front while maintaining diversity. The methodology executes in serial stages: running an EMOA to get a non-dominated set, clustering this set to extract a representative set, calculating pseudo-weights for the representative set, and perturbing the extreme points to generate reference points to drive the ASF towards the Pareto front over iterations until no improvements are found. Initial studies on test problems ZDT1, ZDT2 and ZDT3 show promising results, with the proposed approach finding a representative set of clustered Pareto points in fewer generations compared to NSGA
This document discusses modeling competencies and summarizes a study aimed at better understanding these competencies. It begins by defining modeling as an authentic problem-solving process that moves between reality and mathematics. It then reviews different perspectives on modeling processes and competencies. Specifically, it discusses how modeling competencies involve sub-skills related to setting up models, mathematizing, solving mathematical problems, interpreting results, and validating solutions. The study aimed to examine how well modeling lessons help students independently conduct modeling processes and to identify what full modeling competencies entail. It analyzes student abilities and mistakes to provide insights into modeling competencies.
This document discusses model integration, which involves linking heterogeneous models together into an operational model chain or network. Model integration requires mediation beyond just merging information from different schemas. It discusses how model integration involves assembling tools and methods to generate new knowledge for engineering tasks. Examples shown include stacking ensemble methods using base learners and a meta-learner to combine predictions, as well as using machine learning models as first-stage classifiers with a deep learning model as the ensemble model. The conclusion is that model integration aims to make better decisions by combining results from different classifiers, whether through an integrated model or final decision.
Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Netwo...SYRTO Project
Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Networks - Monica Billio - June 25 2013 - First International Conference on Syrto Project
Financial Symmetry and Moods in the Markets - Jorgen Vitting Andersen - Novem...SYRTO Project
Financial Symmetry and Moods in the Markets - Jorgen Vitting Andersen - November 26 2013 - Seminar at the Department of Economics and Management of the University of Brescia
Bank Interconnectedness What determines the links? - Puriya Abbassi, Christia...SYRTO Project
Bank Interconnectedness What determines the links? - Puriya Abbassi, Christian Brownlees, Christina Hans, Natalia Podlich.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
Opinion mining framework using proposed RB-bayes model for text classicationIJECEIAES
Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.
A preliminary survey on optimized multiobjective metaheuristic methods for da...ijcsit
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
This document discusses probabilistic modeling and decision theory. It describes how probabilistic models can be used to make decisions based on observed data and prior knowledge. The key aspects covered include:
- Probabilistic models express the probability of outcomes given observed data.
- Decision theory involves assigning costs to outcomes and choosing the action that maximizes expected utility.
- Common probabilistic models for decision making include Bayesian networks, neural networks, and support vector machines.
- Bayesian networks represent conditional dependencies graphically and can be used for inference, parameter estimation, and structure learning.
ADABOOST ENSEMBLE WITH SIMPLE GENETIC ALGORITHM FOR STUDENT PREDICTION MODELijcsit
Predicting the student performance is a great concern to the higher education managements.This
prediction helps to identify and to improve students' performance.Several factors may improve this
performance.In the present study, we employ the data mining processes, particularly classification, to
enhance the quality of the higher educational system. Recently, a new direction is used for the improvement
of the classification accuracy by combining classifiers.In thispaper, we design and evaluate a fastlearning
algorithm using AdaBoost ensemble with a simple genetic algorithmcalled “Ada-GA” where the genetic
algorithm is demonstrated to successfully improve the accuracy of the combined classifier performance.
The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,
especially in very large classes. This early prediction allows the instructor to provide appropriate advising
to those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the results
showed that this algorithm hassuccessfully improved the detection accuracy as well as it reduces the
complexity of computation.
This document provides an overview of a survey of multi-objective evolutionary algorithms for data mining tasks. It discusses key concepts in multi-objective optimization and evolutionary algorithms. It also reviews common data mining tasks like feature selection, classification, clustering, and association rule mining that are often formulated as multi-objective problems and solved using multi-objective evolutionary algorithms. The survey focuses on reviewing applications of multi-objective evolutionary algorithms for feature selection and classification in part 1, and applications for clustering, association rule mining and other tasks in part 2.
Discussion of “Network Connectivity and Systematic Risk” and “The Impact of N...SYRTO Project
Discussion of “Network Connectivity and Systematic Risk” and “The Impact of Network Connectivity on Factor Exposures, Asset pricing and Portfolio Diversification” by Billio, Caporin, Panzica and Pelizzon. Arjen Siegmann. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
Systemic and Systematic risk - Monica Billio, Massimiliano Caporin, Roberto Panzica, Loriana Pelizzon
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
A SYSTEM OF SERIAL COMPUTATION FOR CLASSIFIED RULES PREDICTION IN NONREGULAR ...ijaia
Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models,
our previous research work has developed a system of a regular ontology that models learning structures
in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has
led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed
for inductive learning processes and decision making in a multiagent system. But not all processes or
models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict
the required number of rules of a non-regular ontology model given some defined parameters.
This document discusses challenges in comparing structured models using cross-validation. It presents a decision-theoretic framework for model assessment and selection based on expected predictive loss. Different methods for estimating predictive loss are discussed, including k-fold cross-validation which is used to estimate the predictive loss of various multilevel models for a dataset from the Cooperative Congressional Election Survey with deeply nested demographic variables.
Improved probabilistic distance based locality preserving projections method ...IJECEIAES
In this paper, a dimensionality reduction is achieved in large datasets using the proposed distance based Non-integer Matrix Factorization (NMF) technique, which is intended to solve the data dimensionality problem. Here, NMF and distance measurement aim to resolve the non-orthogonality problem due to increased dataset dimensionality. It initially partitions the datasets, organizes them into a defined geometric structure and it avoids capturing the dataset structure through a distance based similarity measurement. The proposed method is designed to fit the dynamic datasets and it includes the intrinsic structure using data geometry. Therefore, the complexity of data is further avoided using an Improved Distance based Locality Preserving Projection. The proposed method is evaluated against existing methods in terms of accuracy, average accuracy, mutual information and average mutual information.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
The document describes a method for tracking objects of deformable shapes in images. It proposes representing the matching of a deformable template to an image as a minimum cost cyclic path in a product space of the template and image. An energy functional is introduced that consists of a data term favoring strong image gradients, a shape consistency term favoring similar tangent angles, and an elastic penalty. Optimization is performed using a minimum ratio cycle algorithm parallelized on GPUs. This provides efficient, pixel-accurate segmentation and correspondence between template and image curve. The method can be extended to 4D to segment and track multiple deformable anatomical structures in medical images.
This document presents a novel (R, S)-norm entropy measure to quantify the degree of fuzziness in intuitionistic fuzzy sets (IFSs). The entropy measure is defined based on (R, S)-norms and is proven to satisfy valid properties of an entropy measure. The entropy measure is then used to propose two decision-making approaches for multi-attribute decision making problems where attribute weights are either partially known or completely unknown. An example is provided to illustrate the decision making process using the novel entropy measure.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document discusses distance similarity measures that can be used for data mining classification and clustering techniques. It proposes a novel distance similarity measure called "Supervised & Unsupervised learning" that uses Euclidean distance similarity to partition training data into clusters. It then builds decision trees on each cluster to improve classification performance. The document also discusses using these measures for other applications like image processing, where k-means clustering can be used to segment images into clusters of similar pixel intensities. In conclusion, it states these similarity measures can help analyze complex datasets for business analysis purposes.
The document proposes a methodology to improve evolutionary multi-objective algorithms (EMOAs) by incorporating achievement scalarizing functions (ASFs) to provide convergence to the Pareto optimal front while maintaining diversity. The methodology executes in serial stages: running an EMOA to get a non-dominated set, clustering this set to extract a representative set, calculating pseudo-weights for the representative set, and perturbing the extreme points to generate reference points to drive the ASF towards the Pareto front over iterations until no improvements are found. Initial studies on test problems ZDT1, ZDT2 and ZDT3 show promising results, with the proposed approach finding a representative set of clustered Pareto points in fewer generations compared to NSGA
This document discusses modeling competencies and summarizes a study aimed at better understanding these competencies. It begins by defining modeling as an authentic problem-solving process that moves between reality and mathematics. It then reviews different perspectives on modeling processes and competencies. Specifically, it discusses how modeling competencies involve sub-skills related to setting up models, mathematizing, solving mathematical problems, interpreting results, and validating solutions. The study aimed to examine how well modeling lessons help students independently conduct modeling processes and to identify what full modeling competencies entail. It analyzes student abilities and mistakes to provide insights into modeling competencies.
This document discusses model integration, which involves linking heterogeneous models together into an operational model chain or network. Model integration requires mediation beyond just merging information from different schemas. It discusses how model integration involves assembling tools and methods to generate new knowledge for engineering tasks. Examples shown include stacking ensemble methods using base learners and a meta-learner to combine predictions, as well as using machine learning models as first-stage classifiers with a deep learning model as the ensemble model. The conclusion is that model integration aims to make better decisions by combining results from different classifiers, whether through an integrated model or final decision.
Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Netwo...SYRTO Project
Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Networks - Monica Billio - June 25 2013 - First International Conference on Syrto Project
Financial Symmetry and Moods in the Markets - Jorgen Vitting Andersen - Novem...SYRTO Project
Financial Symmetry and Moods in the Markets - Jorgen Vitting Andersen - November 26 2013 - Seminar at the Department of Economics and Management of the University of Brescia
Bank Interconnectedness What determines the links? - Puriya Abbassi, Christia...SYRTO Project
Bank Interconnectedness What determines the links? - Puriya Abbassi, Christian Brownlees, Christina Hans, Natalia Podlich.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
Understanding Excessive Risk Taking Seen in Experiments on Financial Markets ...SYRTO Project
This document summarizes research into excessive risk taking in financial market experiments. It describes how experiments were conducted with groups of traders with different risk profiles, finding that groups of all men tended to take the most risks and create speculative states. A model called the $-Game is presented as a way to understand fluctuations and symmetry breaking seen in the experiments. The concept of using an agent-based model to measure the "temperature" of the market's internal state is also introduced.
Measuring the behavioral component of financial fluctuaction. An analysis bas...SYRTO Project
This document summarizes a study that measures the behavioral component of financial market fluctuations using a model with two types of investors - rational investors who maximize expected utility, and behavioral investors who have S-shaped utility functions. The model blends the asset selections of these two investor types using a Bayesian approach, with the rational investor preferences as the prior and behavioral investor preferences as the conditional. An empirical analysis is conducted using the S&P 500 to estimate the optimal weighting parameter between the two investor types that maximizes past cumulative returns.
A new class of models for rating data - Marica Manisera, Paola Zuccolotto, Se...SYRTO Project
A new class of models for rating data - Marica Manisera, Paola Zuccolotto, September 4, 2013. 2013 International Conference of the Royal Statistical Society
Discussion of “Limits to Arbitrage in Sovereign Bonds” by Loriana Pelizzon, M...SYRTO Project
Discussion of “Limits to Arbitrage in Sovereign Bonds” by Loriana Pelizzon, Marti G. Subrahmanyam, Davide Tomio, and Jun Uno - Puriya Abbassi.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
Sovereign credit risk, liquidity, and the ecb intervention: deus ex machina? ...SYRTO Project
Sovereign credit risk, liquidity, and the ecb intervention: deus ex machina? - Loriana Pelizzon, Marti Subrahmanyam, Davide Tomio, Jun Uno. June, 5 2014. First International Conference on Sovereign Bond Markets.
Public Debt Sustainability in Italy: Problems and Proposals - Paolo Manasse. ...SYRTO Project
Public Debt Sustainability in Italy: Problems and Proposals - Paolo Manasse
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
A Dynamic Factor Model: Inference and Empirical Application. Ioannis Vrontos SYRTO Project
The document describes a dynamic factor model to analyze how financial risks are interconnected within the Eurozone. It uses the model to examine risk dynamics using sovereign CDS and equity returns from 2007-2009 covering the US financial crisis and pre-sovereign crisis in Europe. The model relates asset returns to latent sector factors, macro factors, and covariates. Bayesian inference is applied using MCMC to estimate the time-varying parameters and latent factors.
Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time...SYRTO Project
Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models. Andre Lucas. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
1) The document discusses a framework for modeling systemic risk and banking crises through the lens of a macroeconomic model. It aims to better understand the dynamics of financial and real business cycles.
2) Key findings from the model include that banking crises are typically preceded by unusually long periods of positive productivity shocks that fuel credit booms, and then peter out, leading to over-savings and fragile banks.
3) Next steps discussed include how to design optimal macroprudential policies like countercyclical capital buffers to address externalities and mitigate systemic risk, through tools like regulatory requirements and coordinated monetary/regulatory policies.
The main objective of this work is to facilitate the identification, sharing, and reasoning about cerebral tumors observations via the formalization of their semantic meanings in order to facilitate their exploitation in both the clinical practice and research. We focused our analysis on the VASARI terminology as a proof of concept, but we are convinced that our work can be useful in other biomedical imaging contexts.
Beyond Broken Stick Modeling: R Tutorial for interpretable multivariate analysisPetteriTeikariPhD
This document provides information about Petteri Teikari, including his educational background and affiliation with the Singapore Eye Research Institute. It then lists several papers and resources related to broken stick modeling, nonlinear multivariate analysis, and variable importance measures in random forests. Specific topics covered include dynamic modeling of multivariate processes, joint frailty models, additive modeling, outcome weighted deep learning for combination therapies, survival trees, correlation and variable importance, and developing model-agnostic variable importance measures. Links are provided to papers, code implementations, and visualization resources.
201 - Using Qualitative Metasummary to Synthesize Empirical Findings in Liter...ESEM 2014
This document describes a study that used qualitative metasummary to synthesize findings from multiple empirical studies on software engineering teams. It discusses the metasummary method, which involves extracting findings, grouping them, abstracting them, and calculating frequency and intensity effect sizes. The researchers applied this method to studies on software engineering team performance. They found it produced a synthesis highly connected to the original findings but had limitations in comparability and integrating mixed data. Overall, qualitative metasummary was found to be useful for literature reviews in software engineering but could be improved.
This document describes a qualitative metasummary method for synthesizing findings from mixed-method literature reviews. The method involves extracting findings from primary studies, grouping similar findings, abstracting the findings under descriptive labels, and calculating frequency and intensity effect sizes. The authors apply this method to studies on software engineering team performance. They find that the method produces transparent, auditible results well-connected to primary studies, but that calculating effect sizes is too simplistic and comparability between studies is challenging.
This thesis aims to formulate a simple measurement to evaluate and compare the predictive distributions of out-of-sample forecasts between autoregressive (AR) and vector autoregressive (VAR) models. The author conducts simulation studies to estimate AR and VAR models using Bayesian inference. A measurement is developed that uses out-of-sample forecasts and predictive distributions to evaluate the full forecast error probability distribution at different horizons. The measurement is found to accurately evaluate single forecasts and calibrate forecast models.
This document describes a study that develops a fuzzy inference system (FIS) to assess the sustainability of biomass production for energy purposes. The FIS uses four input parameters - energy output, energy balance ratio, fertilizer usage, and pesticide usage - with defined membership functions. Eighty-one IF-THEN rules were created relating the input parameters to a single output parameter, a fuzzy sustainability index (FSI). The FSI indicates the sustainability level as very low, low, medium, high or very high. The FIS provides a means to evaluate biomass sustainability that can handle uncertain input data, unlike other assessment methods. Graphs show the relationship between input parameters and the fuzzy output based on the rules.
[MMIR@MM2023] On Popularity Bias of Multimodal-aware Recommender Systems: A M...Daniele Malitesta
Slides for the paper "On Popularity Bias of Multimodal-aware Recommender Systems: A Modalities-driven Analysis", accepted and presented at the 1st International Workshop on Deep Multimodal Learning for Information Retrieval, co-located with the 31st ACM International Conference on Multimedia (MMIR@MM'23).
Paper: https://dl.acm.org/doi/abs/10.1145/3606040.3617441
Code: https://github.com/sisinflab/MultiMod-Popularity-Bias
A COMPARISON STUDY OF ESTIMATION METHODS FOR GENERALIZED JELINSKI-MORANDA MOD...ijseajournal
In this paper three methods for estimating the parameters of the generalized Jelinski-Moranda (GJM)
model are compared. The needed mathematical formulas for resolving the estimates are derived.
Because of the lack of various real data with changed input data size different simulation scenarios are
given to help achieving our goals. Illustrative algorithms for the simulation studies are given. First, the
accuracy of the GJ-M model’s estimators is checked based on two evaluation criteria. Moreover, several
generated models from the GJ-M general formula are evaluated based on three different methods of
comparison. Useful results for the software reliability modelling area are concluded.
doctoral study prospectus9Nature of the StudyTo conduct DustiBuckner14
doctoral study prospectus
9
Nature of the Study
To conduct the current study, qualitative, quantitative, and mixed research approaches were considered. I selected the quantitative method because it helps test hypotheses through a deductive approach. Quantitative method involves measuring constructs through quantitative variables and statistical tools to test hypotheses to address research questions (O'Dwyer & Bernauer, 2016). In contrast, qualitative research is characterized by an inductive approach, where perceptions and subjective experiences of individuals are used to develop themes of a research phenomenon (Östlund et al., 2011). The mixed approach, which involves aspects from both the quantitative and the qualitative approaches, is useful in studies specifically suited for that purpose, as such a combination involves limitations inherent in both approaches (Bryman, 2006). I will not use the qualitative method as the purpose of this study does not require an inductive approach, nor will I use mixed methods approach as the additional requirements for qualitative elements are not necessary in this study.
For research design, I considered descriptive design and correlational design. I selected the correlational design because the purpose of this study involves examining the relationship between variables. Correlational research design entails the measurement of two or more relevant variables, as well as assessing the relationship between variables (Crawford, 2014). In contrast, descriptive research design is used to gather quantifiable data and describe the nature of a demographic segment (Mertens, 2014). I will not use descriptive research design as the purpose in this study is not to describe the nature of employees at the selected business organization but to examine the relationship between transformational leadership components, namely idealized influence, inspirational motivation, and individualized consideration and employee retention.
References
Anitha, J., & Begum, F. N. (2016). Role of organisational culture and employee commitment in employee retention. ASBM Journal of Management, 9(1), 17-28. https://www.semanticscholar.org/paper/Role-of-Organisational-Culture-and-Employee-in-Anitha-Begum/78f5caf30944c582f3c1fe4f8ae82f77d6a9cafd
Avolio, B., & Bass, B. (2002). Developing potential across a full range of leadership cases on transactional and transformational leadership. Lawrence Erlbaum Associates.
Avolio, B., Waldman, D., & Yammarino, F. (1991). Leading in the 1990s: The four I’s of transformational leadership. Journal of European Industrial Training, 15(4), 9-16. https://doi.org/10.1108/03090599110143366
Boamah, S. A., Laschinger, H. K. S., Wong, C., & Clarke, S. (2018). Effect of transformational leadership on job satisfaction and patient safety outcomes. Nursing Outlook, 66(2), 180-189. https://doi.org/10.1016/j.outlook.2017.10.004
Bryman, A. (2006). Integrating quantitative and qualitative research: How is it done? Quali ...
Incremental Sense Weight Training for Contextualized Word Embedding Interpret...Jinho Choi
In this work, we propose a new training procedure for learning the importance of dimensions of word embeddings in representing word meanings. Our algorithm advanced in the interpretation filed of word embeddings, which are extremely critical in the NLP filed due to the lack of understanding of word embeddings despite their superior ability in progressing NLP tasks. Although previous work has investigated in the interpretability of word embeddings through imparting interpretability to the embedding training models or through post-processing procedures of pre-trained embeddings, our algorithm proposes a new perspective to word embedding dimension interpretation where each dimension gets evaluated and can be visualized. Also, our algorithm adheres to a novel assumption that not all dimensions are necessary for representing a word sense (word meaning) and dimensions that are negligible get discarded, which have not been attempted in previous studies.
Efficiency of Prediction Algorithms for Mining Biological DatabasesIOSR Journals
This document analyzes the efficiency of various prediction algorithms for mining biological databases. It discusses prediction through mining biological databases to identify disease risks. It then evaluates several prediction algorithms (ZeroR, OneR, JRip, PART, Decision Table) on a breast cancer dataset using measures like accuracy, sensitivity, specificity, and predictive values. The results show that the JRip and PART algorithms generally had the highest accuracy rates, around 70%, while ZeroR had the lowest accuracy. However, ZeroR had a perfect positive predictive value. The study aims to assess the most efficient algorithms for predictive mining of biological data.
Accounting for variance in machine learning benchmarksDevansh16
Accounting for Variance in Machine Learning Benchmarks
Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.
This paper proposes a novel model for imbalanced data classification that integrates data sampling, data space improvement, cost-sensitive learning, and ensemble learning. The model first constructs balanced data blocks from the imbalanced dataset using undersampling and oversampling. It then applies metric learning to improve the data space by bringing similar samples closer together and separating different classes. An adaptive weighting component calculates class weights to address incorrectly labeled samples. Finally, multiple base classifiers are combined through weighted voting to produce the final predictions. Experimental results on 14 public datasets show the proposed model outperforms state-of-the-art methods in terms of several evaluation metrics.
A Periodical Production Plan for Uncertain Orders in a Closed-Loop Supply Cha...IJERA Editor
This document proposes fuzzy set theory models to address production planning under uncertain demand in a closed-loop supply chain system. Specifically, it develops a Fuzzy Chance-Constrained Production Mix Model (FCCPMM) that uses fuzzy set concepts like possibility distributions and α-cut sets to formulate constraints allowing the decision maker to account for demand uncertainty in an optimization model seeking to maximize profit. The model is demonstrated through a numerical example and is intended to help producers better cope with production risks from uncertain customer orders in a closed-loop supply chain context.
Modelling the expected loss of bodily injury claims using gradient boostingGregg Barrett
This document summarizes an effort to model the expected loss of bodily injury claims using gradient boosting. Frequency and severity models are built separately and then combined to estimate expected loss. Gradient boosting is chosen as the modeling approach due to its flexibility. Tuning parameters like shrinkage, number of trees, and depth must be selected. The goal is predictive accuracy over interpretability. Performance is evaluated on a test set not used for model selection.
This document provides an overview and preview of Chapter 9 from the textbook Elementary Statistics. Chapter 9 discusses methods for making statistical inferences when there are two samples or populations, extending the techniques introduced in Chapters 7 and 8 which dealt with single samples or populations. The chapter covers topics like comparing two proportions, comparing two means from independent samples, comparing two dependent samples using matched pairs, and comparing two variances or standard deviations. Examples of applications discussed include comparing weight gain in college freshmen, comparing polio rates in children given a vaccine or placebo, and comparing cholesterol levels in subjects given Lipitor or a placebo.
Similar to Ensemble models: theory and applications, Figini, Vezzoli. September, 3 2013 (20)
Predicting the economic public opinions in EuropeSYRTO Project
Predicting the economic public opinions in Europe
Maurizio Carpita, Enrico Ciavolino, Mariangela Nitti
University of Brescia & University of Salento
SYRTO Project Final Conference, Paris – February 19, 2016
Scalable inference for a full multivariate stochastic volatilitySYRTO Project
Scalable inference for a full multivariate stochastic volatility
P. Dellaportas, A. Plataniotis and M. Titsias UCL(London), AUEB(Athens), AUEB(Athens)
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Network and risk spillovers: a multivariate GARCH perspectiveSYRTO Project
M. Billio, M. Caporin, L. Frattarolo, L. Pelizzon: “Network and risk spillovers: a multivariate GARCH perspective”.
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Clustering in dynamic causal networks as a measure of systemic risk on the eu...SYRTO Project
Clustering in dynamic causal networks as a measure of systemic risk on the euro zone
M. Billio, H. Gatfaoui, L. Frattarolo, P. de Peretti
IESEG/ Universitè Paris1 Panthèon-Sorbonne/ University Ca' Foscari
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Entropy and systemic risk measures
M. Billio, R. Casarin, M. Costola, A. Pasqualini
Ca’ Foscari Venice University
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Results of the SYRTO Project
Roberto Savona - Primary Coordinator of the SYRTO Project
University of Brescia
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Comment on:Risk Dynamics in the Eurozone: A New Factor Model forSovereign C...SYRTO Project
Comment on:Risk Dynamics in the Eurozone: A New Factor Model forSovereign CDS and Equity Returnsby Dellaportas, Meligkotsidou, Savona, Vrontos. Andre Lucas. Amsterda, June, 25 2015. Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models. Andre Lucas. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
Spillover dynamics for sistemic risk measurement using spatial financial time...SYRTO Project
Spillover dynamics for sistemic risk measurement using spatial financial time series models. Julia Schaumburg, Andre Lucas, Siem Jan Koopman, and Francisco Blasques. ESEM - Toulouse, August 25-29, 2014
http://www.eea-esem.com/eea-esem/2014/prog/viewpaper.asp?pid=1044
Measuring the behavioral component of financial fluctuation: an analysis bas...SYRTO Project
Measuring the behavioral component of financial fluctuation: an analysis based on the S&P500 - Caporin M., Corazzini L., Costola M. June, 27 2013. IFABS 2013 - Posters session.
The microstructure of the european sovereign bond market. Loriana Pellizzon. ...SYRTO Project
This study analyzes the microstructure of the European sovereign bond market during the Eurozone crisis between 2011-2012. It finds that credit risk, as measured by CDS spreads, is non-linearly related to market liquidity, as higher credit risk leads to much greater illiquidity. Market makers temporarily stopped participating when CDS spreads widened significantly. ECB interventions successfully reduced solvency concerns and improved liquidity. The analysis uses a unique high-frequency dataset of order and trade data from the Italian sovereign bond market, the largest in the Eurozone, to examine changes in liquidity measures like bid-ask spreads and quote quantities around periods of financial stress.
Time-Varying Temporal Dependene in Autoregressive Models - Francisco Blasques...SYRTO Project
Time-Varying Temporal Dependene in Autoregressive Models - Francisco Blasques, Siem Jan Koopman, Andre Lucas. June 2014. International Association for Applied Econometrics Annual Conference
Maximum likelihood estimation for generalized autoregressive score models - A...SYRTO Project
Maximum likelihood estimation for generalized autoregressive score models - Andre Lucas, Francisco Blasques, Siem Jan Koopman. June 2014. International Association for Applied Econometrics Annual Conference
Score-driven models for forecasting - Blasques F., Koopman S.J., Lucas A.. Ju...
Ensemble models: theory and applications, Figini, Vezzoli. September, 3 2013
1. Ensemble models: theory and applications
SYstemic Risk TOmography:
Signals, Measurements, Transmission Channels, and Policy Interventions
Silvia Figini
University of Pavia, Italy
Marika Vezzoli
University of Brescia, Italy
Royal Statistic Society Conference 2013
2. Conference 2013 September 3 – 5, 2013 Newcastle UK
SYRTO Project
Sovereigns
Banks and other Financial Intermediaries (BFIs)
Corporations This study is part of the SYRTO Project which is funded by the European Union (EU) under the 7th Framework Programme (FP7-SSH/2007-2013) Focusing on the European Union the project explores the relationships between (and among)
Silvia Figini, Marika Vezzoli
3. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
SYRTO Project: Two main objectives Identify the common and the sector-specific (idiosyncratic) risks, and assemble a web-based Early Warnings System (EWS) to be used as: Risk Barometer for each sector and countries alike, in order to identify potential threats to financial stability a system of Rules of Thumb by monitoring a series of leading indicators so as to minimise the possible negative impacts from systemic crises
1. EWS
2. Syrto Code Realize the SYRTO Code in order to detect a series of recommendations, also expressed in terms of EWS prescriptions, on: the appropriate governance structures for EU to prevent and minimise systemic risks the best mechanisms for ensuring an effective interplay between, and coordination of, macro and micro-prudential responsibilities
4. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
SYRTO Project: Who we are
Consortium Advisory Board University of Brescia Centre National de la Recherche Scientifique (CNRS) Athens University of Economics and Business – Research Center University Cà Foscari Venice University of Amsterdam Stichting VU-VUMC (VUA)
1.Scientific Division Research Unit (among others: P. Balduzzi, A. W. Lo) Supervisory Unit (among others: R. Engle, Y. Aït-Sahalia, D. Duffie, P. Embrechts)
2.Policy Division ECB, ESRB, IMF BIS, D. Bundesbank, EBA, EC, OECD, Sveriges Riksbank
5. Silvia Figini, Marika Vezzoli
Conference 2013 September 3 – 5, 2013 Newcastle UK
Introduction In this study we investigate ensemble learning and classical model averaging in order to obtain a well calibrated credit risk model in terms of predictive accuracy We compare ensemble learning approaches, like Random Forest (Breiman, 2001) with Bayesian Model Averaging (BMA) (e.g. Steel, 2011). The final aim is to improve the predictive performance of the models With a special focus on credit risk application, few papers have investigated the comparison between single selected models and model averaging. In the parametric framework, we recall the paper of Hayden et al. (2009) which presents a comparison between stepwise selection in logistic regression and BMA (Madigan et al. 1999) and Tsai et al. (2010) that show a statistical criterion and a financial market measure to compare the forecasting accuracy of different model selection approaches In the non parametric framework, we recall the papers of Figini and Fantazzini (2009) and Zhang et al. (2010)
6. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
Main objectives Non Parametric framework: comparing single model based on classification tree with Random Forest Parametric framework: comparing single model based on logistic regression with BMA Proposing some ideas on which models we should include in the pool of models in order to make a coherent averaging in terms of predictive capability, discriminatory power, stability of the results
7. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
Non Parametric methods based on Random Forests In the non parametric framework, the ensemble learning techniques combine poor predictors, like trees, in order to obtain robust forecasting Schapire (1990) showed that weak learner could always improve its performance by training two additional predictors on filtered versions of the input data, while Breiman (2001) generated multiple predictors combining them by simple averaging (regression) or voting (classification) In this study, we focus our attention on Random Forest (RF) where every weak learner is obtained by growing a non pruned tree on a training set which is a different bootstrap sample drawn from the data We have chosen Random Forest because it provides an accuracy level that is in line with Boosting algorithm with better performance in terms of computational time Breiman (2001)
8. Silvia Figini, Marika Vezzoli
Conference 2013 September 3 – 5, 2013 Newcastle UK
Parametric methods based on Bayesian Model Averaging
9. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
Prior selection
10. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
Bayesian Model Averaging BMA can be summarized in the following steps: Given q variables, we fit all the possible variables combination and we obtain the model space M of dimension 2q For each model we compute its marginal likelihood We assume a prior on the model space, as in Ley and Steel (2009), with a specific setting of the hyper parameters involved For each model we obtain the posterior model probability We fit each model on the data at hand and the final forecast for a specific observation is the average of the prediction made by each model weighted by the relative posterior model probability
11. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
Model space in the parametric framework: an example with 4 variables Model space M 24 = 16
12. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
Predictive measures of performance In order to detect the predictive capability of a single model with respect to averaged models based on BMA or RF, we shall consider the Receiver Operating Characteristic curve (ROC), the area under it (AUC) and the H measure (e.g. Hand et al. 2010) The discriminant power of a predictive model can be measured by a confusion matrix (Kohavi and Provost, 1998), which compares actual and predicted classifications for a fixed cut-off We have derive different cut-offs resorting to the minimisation of the difference between sensitivity and specificity (P fair in Schrder and Richter 1999) or to the maximisation of the correct classification rate (P opt, calculated from the ROC as described in Zweig and Campbell (1993) taking into account different costs of false positive or false negative predictions). We have use also a cut-off = 0.5
13. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
The data In this study we focus on a real data base provided by Creditreform and previously analysed in Figini and Fantazzini (2009) The data set is composed of about 800 SMEs, 9 quantitative independent variables and a binary target variable (default) The a priori probability of default is equal at 12.5%
14. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
Assessment of Single and Averaged Models
15. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
Selection of Single and Averaged Models based on AUC Following DeLong et all. (1998), we compare the AUCs between pairs of models. We obtain that: AUCTree ≠ AUCRandom Forest (p-value < 0.05) AUCTree ≠ AUCBMA (p-value < 0.05) while all the remaining comparison are not statistical different
16. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
Prior on the model space and BMA
17. Silvia Figini, Marika Vezzoli
Conference 2013
September 3 – 5, 2013
Newcastle UK
Discriminatory Power
18. Silvia Figini, Marika Vezzoli
Conference 2013 September 3 – 5, 2013 Newcastle UK
Remarks and Conclusions Bayesian Model Averaging Both the Binomial and the Binomial-Beta priors have in common the implicit assumption that the probability of one regressors appears in the model is independent of the inclusion of others whereas regressors are typically correlated (e.g. Durlauf et al. 2008) It is interesting to focus on how different priors settings affect the predictive performances of the averaged models Random Forest On the basis of the results at hand we underline that also in the non parametric framework averaged models perform better that single model It is interesting to compare the results at hand with different ensemble methods to optimise the accuracy of the averaged model
19. This project has received funding from the European Union’s
Seventh Framework Programme for research, technological
development and demonstration under grant agreement n° 320270
www.syrtoproject.eu
This document reflects only the author’s views.
The European Union is not liable for any use that may be made of the information contained therein.