This document presents a genetic algorithm approach to generating synthetic data sets for analyzing classifier behavior. The genetic algorithm represents data set labelings as binary strings and uses genetic operators like crossover and mutation to evolve solutions that satisfy the desired complexity based on class boundary length. Experiments show the genetic algorithm can generate intermediate complexity data sets in early generations and produce similar accuracy rates across different classifier paradigms, while allowing control over the data set properties. Future work aims to improve efficiency and scalability, enable multiple criteria optimization, and develop benchmark problems with more realistic structure.
A New Model for Credit Approval Problems a Neuro Genetic System with Quantum ...Anderson Pinho
This paper presents a new model for neuro-evolutionary systems. It is a new quantum-inspired evolutionary algorithm with binary-real representation (QIEA-BR) for evolution of a neural network. The proposed model is an extension of the QIEA-R developed for numerical optimization. The Quantum-Inspired Neuro-Evolutionary Computation model (QINEA-BR) is able to completely configure a feed-forward neural network in terms of selecting the relevant input variables, number of neurons in the hidden layer and all existent synaptic weights. QINEA-BR is evaluated in a benchmark problem of financial credit evaluation. The results obtained demonstrate the effectiveness of this new model in comparison with other machine learning and statistical models, providing good accuracy in separating good from bad customers.
Image segmentation is a classic computer vision task that aims at labeling pixels with semantic classes. These slides provide an overview of the basic approaches applied from the deep learning field to tackle this challenge and presents the basic subtasks (semantic, instance and panoptic segmentation) and related datasets.
Presented at the International Summer School on Deep Learning (ISSonDL) 2020 held online and organized by the University of Gdansk (Poland) between the 30th August and 2nd September.
http://2020.dl-lab.eu/virtual-summer-school-on-deep-learning/
Memetic Algorithms have become one of the key methodologies behind solvers that are capable of tackling very large, real-world, optimisation problems. They are being actively investigated in research institutions as well as broadly applied in industry. In this talk we provide a pragmatic guide on the key design issues underpinning Memetic Algorithms (MA) engineering. We begin with a brief contextual introduction to Memetic Algorithms and then move on to define a Pattern Language for MAs. For each pattern, an associated design issue is tackled and illustrated with examples from the literature. We then fast forward to the future and mention what, in our mind, are the key challenges that scientistis and practitioner will need to face if Memetic Algorithms are to remain a relevant technology in the next 20 years.
Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep neural representation. This tutorial will firstly review the basic neural architectures to encode and decode vision, text and audio, to later review the those models that have successfully translated information across modalities. The contents of this tutorial are available at: https://telecombcn-dl.github.io/2019-mmm-tutorial/.
These slides summarize the main trends in deep neural networks for video encoding. Including single frame models, spatiotemporal convolutionals, long term sequence modeling with RNNs and their combinaction with optical flow.
Data-centric AI and the convergence of data and model engineering: opportunit...Paolo Missier
A keynote talk given to the IDEAL 2023 conference (Evora, Portugal Nov 23, 2023).
Abstract.
The past few years have seen the emergence of what the AI community calls "Data-centric AI", namely the recognition that some of the limiting factors in AI performance are in fact in the data used for training the models, as much as in the expressiveness and complexity of the models themselves. One analogy is that of a powerful engine that will only run as fast as the quality of the fuel allows. A plethora of recent literature has started the connection between data and models in depth, along with startups that offer "data engineering for AI" services. Some concepts are well-known to the data engineering community, including incremental data cleaning, multi-source integration, or data bias control; others are more specific to AI applications, for instance the realisation that some samples in the training space are "easier to learn from" than others. In this "position talk" I will suggest that, from an infrastructure perspective, there is an opportunity to efficiently support patterns of complex pipelines where data and model improvements are entangled in a series of iterations. I will focus in particular on end-to-end tracking of data and model versions, as a way to support MLDev and MLOps engineers as they navigate through a complex decision space.
Workshop nwav 47 - LVS - Tool for Quantitative Data AnalysisOlga Scrivner
In the format of hands-on session, this workshop will introduce participants to the Language Variation Suite (LVS), a user-friendly interactive web application built in R. LVS provides access to advanced statistical methods and visualization techniques, such as mixed-effects modeling, conditional and random tree analyses, cluster analysis. These advanced methods enable researchers to handle imbalanced data, measure individual and group variation, estimate significance, and rank variables according to their significance.
A New Model for Credit Approval Problems a Neuro Genetic System with Quantum ...Anderson Pinho
This paper presents a new model for neuro-evolutionary systems. It is a new quantum-inspired evolutionary algorithm with binary-real representation (QIEA-BR) for evolution of a neural network. The proposed model is an extension of the QIEA-R developed for numerical optimization. The Quantum-Inspired Neuro-Evolutionary Computation model (QINEA-BR) is able to completely configure a feed-forward neural network in terms of selecting the relevant input variables, number of neurons in the hidden layer and all existent synaptic weights. QINEA-BR is evaluated in a benchmark problem of financial credit evaluation. The results obtained demonstrate the effectiveness of this new model in comparison with other machine learning and statistical models, providing good accuracy in separating good from bad customers.
Image segmentation is a classic computer vision task that aims at labeling pixels with semantic classes. These slides provide an overview of the basic approaches applied from the deep learning field to tackle this challenge and presents the basic subtasks (semantic, instance and panoptic segmentation) and related datasets.
Presented at the International Summer School on Deep Learning (ISSonDL) 2020 held online and organized by the University of Gdansk (Poland) between the 30th August and 2nd September.
http://2020.dl-lab.eu/virtual-summer-school-on-deep-learning/
Memetic Algorithms have become one of the key methodologies behind solvers that are capable of tackling very large, real-world, optimisation problems. They are being actively investigated in research institutions as well as broadly applied in industry. In this talk we provide a pragmatic guide on the key design issues underpinning Memetic Algorithms (MA) engineering. We begin with a brief contextual introduction to Memetic Algorithms and then move on to define a Pattern Language for MAs. For each pattern, an associated design issue is tackled and illustrated with examples from the literature. We then fast forward to the future and mention what, in our mind, are the key challenges that scientistis and practitioner will need to face if Memetic Algorithms are to remain a relevant technology in the next 20 years.
Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep neural representation. This tutorial will firstly review the basic neural architectures to encode and decode vision, text and audio, to later review the those models that have successfully translated information across modalities. The contents of this tutorial are available at: https://telecombcn-dl.github.io/2019-mmm-tutorial/.
These slides summarize the main trends in deep neural networks for video encoding. Including single frame models, spatiotemporal convolutionals, long term sequence modeling with RNNs and their combinaction with optical flow.
Data-centric AI and the convergence of data and model engineering: opportunit...Paolo Missier
A keynote talk given to the IDEAL 2023 conference (Evora, Portugal Nov 23, 2023).
Abstract.
The past few years have seen the emergence of what the AI community calls "Data-centric AI", namely the recognition that some of the limiting factors in AI performance are in fact in the data used for training the models, as much as in the expressiveness and complexity of the models themselves. One analogy is that of a powerful engine that will only run as fast as the quality of the fuel allows. A plethora of recent literature has started the connection between data and models in depth, along with startups that offer "data engineering for AI" services. Some concepts are well-known to the data engineering community, including incremental data cleaning, multi-source integration, or data bias control; others are more specific to AI applications, for instance the realisation that some samples in the training space are "easier to learn from" than others. In this "position talk" I will suggest that, from an infrastructure perspective, there is an opportunity to efficiently support patterns of complex pipelines where data and model improvements are entangled in a series of iterations. I will focus in particular on end-to-end tracking of data and model versions, as a way to support MLDev and MLOps engineers as they navigate through a complex decision space.
Workshop nwav 47 - LVS - Tool for Quantitative Data AnalysisOlga Scrivner
In the format of hands-on session, this workshop will introduce participants to the Language Variation Suite (LVS), a user-friendly interactive web application built in R. LVS provides access to advanced statistical methods and visualization techniques, such as mixed-effects modeling, conditional and random tree analyses, cluster analysis. These advanced methods enable researchers to handle imbalanced data, measure individual and group variation, estimate significance, and rank variables according to their significance.
A SURVEY ON DATA MINING IN STEEL INDUSTRIESIJCSES Journal
In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurancetechniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.
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Hypothesis on Different Data Mining AlgorithmsIJERA Editor
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Decision Tree, Artificial Neural Network & Support Vector Machine are studied & examined using benchmark
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Classifier Model using Artificial Neural NetworkAI Publications
When it comes to AI and ML, precision in categorization is of the utmost importance. In this research, the use of supervised instance selection (SIS) to improve the performance of artificial neural networks (ANNs) in classification is investigated. The goal of SIS is to enhance the accuracy of future classification tasks by identifying and selecting a subset of examples from the original dataset. The purpose of this research is to provide light on how useful SIS is as a preprocessing tool for artificial neural network-based classification. The work aims to improve the input dataset to ANNs by using SIS, which may help with problems caused by noisy or redundant data. The ultimate goal is to improve ANNs' ability to identify data points properly across a wide range of application areas.
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http://sandymillin.wordpress.com/iateflwebinar2024
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This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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HIS'2008: Genetic-based Synthetic Data Sets for the Analysis of Classifiers Behavior
1. Genetic-based Synthetic Data
Sets for the A l i f
S t f th Analysis of
Classifiers Behavior
8th I t
International Conference on Hybrid Intelligent Systems
ti lC f H b id I t lli tS t
Núria Macià
Albert Orriols-Puig
Alb t O i l P i
Ester Bernadó-Mansilla
{nmacia,aorriols,esterb}@salle.url.edu
Grup de Recerca en Sistemes Intel·ligents
Enginyeria i Arquitectura La Salle
Universitat Ramon Llull
2. Motivation
Knowledge
Data Set Model
Extraction
Real-world
Learner
problem
+
Prediction
Necessity of synthetic data sets
To evaluate real learners performance under
controlled scenarios
How to generate synthetic data sets?
Data complexity (Ho & Basu, 2002)
Length of the class boundary (Macià et al., 2008)
Objective: Set of benchmark problems to analyze
learners behavior
Overview and Future Research Slide 2
3. Outline
1.
1 Data complexity
2. Synthetic data sets
3. Design of GA
4.
4 Experiments and results
5. Conclusions and further work
Overview and Future Research Slide 3
4. 1. Data complexity
Length of the class boundary
Build minimum spanning tree (MST) connecting all
the points regardless of class
Count the number of edges joining
opposite classes
it l
Two cases of many points in boundary:
Very interleaved or random data
Linearly separable problem with narrow margins
Overview and Future Research Slide 4
5. 2. Synthetic data sets
Generation procedure
Set the number of instances n, the number of
attributes m and the length of the class boundary
m,
b.
Generate n points di t ib t d randomly and b ild
G t i t distributed dl d build
the MST.
Label the class of each
instances
Overview and Future Research Slide 5
6. 2. Synthetic data sets
Exhaustive search
Labelings grow exponentially with the number of
instances
Heuristic search
Demanded length of the class boundary is not
always achieved
No diverse solutions
Genetic algorithm
G ti l ith
Overview and Future Research Slide 6
7. 3. Design of GA
Knowledge representation
k-ary string where the bit i stores the class label of
the ith instance
Data set i Individual i
Att. 1 Att. 2 … Att. N Class
0.4
04 0.5
05 0.4
04 0
0.2 1.0 0.2 1
011011
0.5 0.3 0.4 1
0.6 0.5 0.4 0
0.7 0.1 1.0 1
0.5 0.3 0.9 1
Overview and Future Research Slide 7
8. 3. Design of GA
Genetic operators
s-wise tournament selection
Two-point crossover
T it
Bit-wise mutation
Fitness function
fitnessi = bobj − bi
Overview and Future Research Slide 8
9. 4. Experiment and results (I)
Synthetic data set generation
Different solutions < Solutions
Population converge
Pop lation con erge to the same sol tion
solution
{0100,1011} are equivalent individuals
Intermediate complexity are obtained i early
It di t l it bt i d in l
generations
Overview and Future Research Slide 9
10. 4. Experiment and results (II)
Analysis of classifiers behavior
Three different paradigms: C4.5, Naïve Bayes, and
SMO
Similar accuracy rates with noticeable variability
Overview and Future Research Slide 10
11. 5. Conclusions
The GA allows us to generate data sets with
the demanded length of the class boundary
Overview and Future Research Slide 11
12. 6. Further work
Efficiency and scalability
Move from simple GA to competent GA
Capacity of satisfying multiple criteria
C f f
Multi-objective strategy
j gy
Achieve structure of real-world problems
Provide a set of benchmark problems
Overview and Future Research Slide 12
13. Genetic-based Synthetic Data
Sets for the A l i f
S t f th Analysis of
Classifiers Behavior
8th I t
International Conference on Hybrid Intelligent Systems
ti lC f H b id I t lli tS t
Núria Macià
Albert Orriols-Puig
Alb t O i l P i
Ester Bernadó-Mansilla
{nmacia,aorriols,esterb}@salle.url.edu
Grup de Recerca en Sistemes Intel·ligents
Enginyeria i Arquitectura La Salle
Universitat Ramon Llull
