Dissertation Data Fusion Summary Poster
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This MSc dissertation evaluated ensemble methods for classifying heterogeneous data sources. Feature-level fusion outperformed decision-level fusion. Dynamic Ensemble Selection (DES) improved the performance of Bagging ensembles using decision-level fusion but reduced performance for feature-level fusion. A weak positive correlation was found between ensemble accuracy and DES cluster diversity, as measured by the Cluster Feature Difference (CFD) metric, for some datasets.
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Cluster
Cluster analysis is an unsupervised learning technique that groups similar data objects into clusters. It finds internal structures within unlabeled data by grouping objects based on their characteristics. Clustering is used to gain insight into data distribution and as a preprocessing step for other algorithms. Some applications of clustering include marketing, land use analysis, insurance risk assessment, and city planning. The quality of clustering depends on how well it separates objects within a cluster from objects in other clusters. Hierarchical clustering creates clusters by iteratively merging or splitting groups of objects based on their distances in a dendrogram.
Clustering in Data Mining
Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.
K means clustering
K-means clustering is an algorithm that groups data points into k clusters based on their attributes and distances from initial cluster center points. It works by first randomly selecting k data points as initial centroids, then assigning all other points to the closest centroid and recalculating the centroids. This process repeats until the centroids are stable or a maximum number of iterations is reached. K-means clustering is widely used for machine learning applications like image segmentation and speech recognition due to its efficiency, but it is sensitive to initialization and assumes spherical clusters of similar size and density.
Clusters techniques
The document discusses different clustering techniques used for grouping large amounts of data. It covers partitioning methods like k-means and k-medoids that organize data into exclusive groups. It also describes hierarchical methods like agglomerative and divisive clustering that arrange data into nested groups or trees. Additionally, it mentions density-based and grid-based clustering and provides algorithms for different clustering approaches.
Data clustring
DATA
Data is any raw material or unorganized information.
CLUSTER
Cluster is group of objects that belongs to a same class.
Cluster is a set of tables physically stored together as one table that shares common columns.
http://phpexecutor.com
Cluster Analysis Introduction
Cluster analysis is a technique used to classify objects into groups called clusters based on their similarities. It has many applications in areas like market research, biology, and image processing. There are different types of clustering methods like partitioning, hierarchical, density-based, and grid-based. The k-means algorithm is a commonly used partitioning method where objects are grouped into k clusters based on their distances from centroid points, which are recalculated in each iteration until cluster memberships stabilize. Cluster analysis helps discover patterns and insights from large datasets.
Clustering in data Mining (Data Mining)
It is a data mining technique used to place the data elements into their related groups. Clustering is the process of partitioning the data (or objects) into the same class, The data in one class is more similar to each other than to those in other cluster.
3.1 clustering
Cluster analysis is an unsupervised machine learning technique that groups similar data objects into clusters. It finds internal structures within unlabeled data by partitioning it into groups based on similarity. Some key applications of cluster analysis include market segmentation, document classification, and identifying subtypes of diseases. The quality of clusters depends on both the similarity measure used and how well objects are grouped within each cluster versus across clusters.
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Cluster
Cluster analysis is an unsupervised learning technique that groups similar data objects into clusters. It finds internal structures within unlabeled data by grouping objects based on their characteristics. Clustering is used to gain insight into data distribution and as a preprocessing step for other algorithms. Some applications of clustering include marketing, land use analysis, insurance risk assessment, and city planning. The quality of clustering depends on how well it separates objects within a cluster from objects in other clusters. Hierarchical clustering creates clusters by iteratively merging or splitting groups of objects based on their distances in a dendrogram.
Clustering in Data Mining
Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.
K means clustering
K-means clustering is an algorithm that groups data points into k clusters based on their attributes and distances from initial cluster center points. It works by first randomly selecting k data points as initial centroids, then assigning all other points to the closest centroid and recalculating the centroids. This process repeats until the centroids are stable or a maximum number of iterations is reached. K-means clustering is widely used for machine learning applications like image segmentation and speech recognition due to its efficiency, but it is sensitive to initialization and assumes spherical clusters of similar size and density.
Clusters techniques
The document discusses different clustering techniques used for grouping large amounts of data. It covers partitioning methods like k-means and k-medoids that organize data into exclusive groups. It also describes hierarchical methods like agglomerative and divisive clustering that arrange data into nested groups or trees. Additionally, it mentions density-based and grid-based clustering and provides algorithms for different clustering approaches.
Data clustring
DATA
Data is any raw material or unorganized information.
CLUSTER
Cluster is group of objects that belongs to a same class.
Cluster is a set of tables physically stored together as one table that shares common columns.
http://phpexecutor.com
Cluster Analysis Introduction
Cluster analysis is a technique used to classify objects into groups called clusters based on their similarities. It has many applications in areas like market research, biology, and image processing. There are different types of clustering methods like partitioning, hierarchical, density-based, and grid-based. The k-means algorithm is a commonly used partitioning method where objects are grouped into k clusters based on their distances from centroid points, which are recalculated in each iteration until cluster memberships stabilize. Cluster analysis helps discover patterns and insights from large datasets.
Clustering in data Mining (Data Mining)
It is a data mining technique used to place the data elements into their related groups. Clustering is the process of partitioning the data (or objects) into the same class, The data in one class is more similar to each other than to those in other cluster.
3.1 clustering
Cluster analysis is an unsupervised machine learning technique that groups similar data objects into clusters. It finds internal structures within unlabeled data by partitioning it into groups based on similarity. Some key applications of cluster analysis include market segmentation, document classification, and identifying subtypes of diseases. The quality of clusters depends on both the similarity measure used and how well objects are grouped within each cluster versus across clusters.
Forest Cover type prediction
The document summarizes an analysis of predicting forest cover types using machine learning models. It describes the dataset containing over 500,000 observations of forest cover types and 12 descriptive variables. Various models were tested including decision forests, boosted decision trees, and neural networks. The best performing models on the test set were an ensemble approach blending multiple models and a one-vs-all decision forest, both achieving over 80% accuracy. Experiments were conducted using Microsoft Azure machine learning services.
Clustering
This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.
What is cluster analysis
Cluster analysis is an unsupervised machine learning technique that groups unlabeled data points into clusters. The goal is to categorize data objects such that objects within a cluster are as similar as possible to each other, and as dissimilar as possible to objects in other clusters. Good clustering produces high quality clusters with high intra-class similarity and low inter-class similarity. Clustering has applications in marketing, land use analysis, insurance, and other domains.
Spss tutorial-cluster-analysis
This document provides an overview of cluster analysis techniques. It defines cluster analysis as classifying cases into homogeneous groups based on a set of variables. The document then discusses how cluster analysis can be used in marketing research for market segmentation, understanding consumer behaviors, and identifying new product opportunities. It outlines the typical steps to conduct a cluster analysis, including selecting a distance measure and clustering algorithm, determining the number of clusters, and validating the analysis. Specific clustering methods like hierarchical, k-means, and deciding the number of clusters using the elbow rule are explained. The document concludes with an example of conducting a cluster analysis in SPSS.
forest-cover-type
This document describes an analysis of forest cover type data using three decision tree algorithms: Naive Bayes Tree, Reduced Error Pruning Tree, and J48 Tree. The goal is to determine which algorithm yields the highest classification accuracy and the optimal parameter settings and bin sizes for each algorithm. The data is explored and preprocessed, including binning real-valued features. Experiments are conducted to evaluate accuracy on training and test sets for different parameter settings and bin sizes. Results show bin sizes between 20-50 yield higher accuracy, and optimal training set parameters are not necessarily optimal for the test set.
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Chapter 11 cluster advanced : web and text mining
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Capter10 cluster basic
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Current clustering techniques
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Cluster analysis
Cluster analysis is a technique used to group objects based on characteristics they possess. It involves measuring the distance or similarity between objects and grouping those that are most similar together. There are two main types: hierarchical cluster analysis, which groups objects sequentially into clusters; and nonhierarchical cluster analysis, which directly assigns objects to pre-specified clusters. The choice of method depends on factors like sample size and research objectives.
Statistical Clustering
This document discusses different approaches to multivariate data analysis and clustering, including nearest neighbor methods, hierarchical clustering, and k-means clustering. It provides examples of using Ward's method, average linkage, and k-means clustering on poverty data to identify potential clusters of countries based on variables like birth rate, death rate, and infant mortality rate. Key lessons are that different linkage methods, distance measures, and data normalizations should be tested and that higher-dimensional data may require different variable spaces or transformations to identify meaningful clusters.
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This document provides an overview of clustering techniques. It defines clustering as grouping a set of similar objects into classes, with objects within a cluster being similar to each other and dissimilar to objects in other clusters. The document then discusses partitioning, hierarchical, and density-based clustering methods. It also covers mathematical elements of clustering like partitions, distances, and data types. The goal of clustering is to minimize a similarity function to create high similarity within clusters and low similarity between clusters.
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Clustering is the process of grouping similar objects together. It allows data to be analyzed and summarized. There are several methods of clustering including partitioning, hierarchical, density-based, grid-based, and model-based. Hierarchical clustering methods are either agglomerative (bottom-up) or divisive (top-down). Density-based methods like DBSCAN and OPTICS identify clusters based on density. Grid-based methods impose grids on data to find dense regions. Model-based clustering uses models like expectation-maximization. High-dimensional data can be clustered using subspace or dimension-reduction methods. Constraint-based clustering allows users to specify preferences.
K means Clustering Algorithm
K-means clustering is an algorithm that groups data points into k number of clusters based on their similarity. It works by randomly selecting k data points as initial cluster centroids and then assigning each remaining point to the closest centroid. It then recalculates the centroids and reassigns points in an iterative process until centroids stabilize. While efficient, k-means clustering has weaknesses in that it requires specifying k, can get stuck in local optima, and is not suitable for non-convex shaped clusters or noisy data.
Cluster analysis
Cluster analysis is a data exploration (mining) tool
for dividing a multivariate dataset into “natural”
clusters (groups). We use the methods to explore
whether previously undefined clusters (groups) may
exist in the dataset.
Cluster analysis
Cluster analysis is used to group objects that are similar to each other within the same cluster and dissimilar to objects in other clusters. It has various applications in marketing, land use, insurance, and city planning. There are different types of data that can be used for cluster analysis, including interval-scaled, binary, ordinal, and ratio variables. Major clustering methods include partitioning methods like k-means clustering and hierarchical clustering. K-means clustering partitions objects into k clusters by assigning objects to the cluster with the nearest centroid.
Cluster analysis
Cluster analysis is a technique used to group objects into clusters based on similarities. There are several major approaches to cluster analysis including partitioning methods, hierarchy methods, density-based methods, and grid-based methods. Partitioning methods construct partitions of the data objects into a set number of clusters by optimizing a chosen criterion, such as k-means and k-medoids clustering algorithms.
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Spss tutorial-cluster-analysis
This document provides an overview of cluster analysis techniques. It defines cluster analysis as classifying cases into homogeneous groups based on a set of variables. The document then discusses how cluster analysis can be used in marketing research for market segmentation, understanding consumer behaviors, and identifying new product opportunities. It outlines the typical steps to conduct a cluster analysis, including selecting a distance measure and clustering algorithm, determining the number of clusters, and validating the analysis. Specific clustering methods like hierarchical, k-means, and deciding the number of clusters using the elbow rule are explained. The document concludes with an example of conducting a cluster analysis in SPSS.
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This document describes an analysis of forest cover type data using three decision tree algorithms: Naive Bayes Tree, Reduced Error Pruning Tree, and J48 Tree. The goal is to determine which algorithm yields the highest classification accuracy and the optimal parameter settings and bin sizes for each algorithm. The data is explored and preprocessed, including binning real-valued features. Experiments are conducted to evaluate accuracy on training and test sets for different parameter settings and bin sizes. Results show bin sizes between 20-50 yield higher accuracy, and optimal training set parameters are not necessarily optimal for the test set.
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1. This document contains a lab report submitted by Selase Kwami for a Geography course. It includes questions about performing GIS functions like clip, spatial join, buffer analysis, and summary statistics in ArcMap using shapefiles of schools and Accra metro data.
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3. Images are included showing examples of output for each
Chapter 11 cluster advanced : web and text mining
This document provides an overview of advanced clustering analysis techniques discussed in Chapter 11 of the textbook "Data Mining: Concepts and Techniques". It begins with an introduction to probability model-based clustering and fuzzy clustering. It then discusses using the EM algorithm for fuzzy clustering and fitting univariate Gaussian mixture models. Next, it covers challenges with clustering high-dimensional data and methods for subspace clustering. It also briefly introduces clustering graphs and network data as well as clustering with constraints. The document concludes with an outline of the chapter.
Fuzzy Clustering(C-means, K-means)
Clustering
Goals of Clustering
Clustering Application
K-means
C-means
Fuzzy Clustering Application
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SKFCM
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This document discusses clustering and the k-means clustering algorithm. It defines clustering as grouping a set of data objects into clusters so that objects within the same cluster are similar to each other but dissimilar to objects in other clusters. The k-means algorithm is described as an iterative process that assigns each object to one of k predefined clusters based on the object's distance from the cluster's centroid, then recalculates the centroid, repeating until cluster assignments no longer change. A worked example demonstrates how k-means partitions 7 objects into 2 clusters over 3 iterations. The k-means algorithm is noted to be efficient but requires specifying k and can be impacted by outliers, noise, and non-convex cluster shapes.
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This document provides an overview of clustering techniques. It defines clustering as grouping a set of similar objects into classes, with objects within a cluster being similar to each other and dissimilar to objects in other clusters. The document then discusses partitioning, hierarchical, and density-based clustering methods. It also covers mathematical elements of clustering like partitions, distances, and data types. The goal of clustering is to minimize a similarity function to create high similarity within clusters and low similarity between clusters.
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Clustering is the process of grouping similar objects together. It allows data to be analyzed and summarized. There are several methods of clustering including partitioning, hierarchical, density-based, grid-based, and model-based. Hierarchical clustering methods are either agglomerative (bottom-up) or divisive (top-down). Density-based methods like DBSCAN and OPTICS identify clusters based on density. Grid-based methods impose grids on data to find dense regions. Model-based clustering uses models like expectation-maximization. High-dimensional data can be clustered using subspace or dimension-reduction methods. Constraint-based clustering allows users to specify preferences.
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Cluster analysis is a data exploration (mining) tool
for dividing a multivariate dataset into “natural”
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whether previously undefined clusters (groups) may
exist in the dataset.
Cluster analysis
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Cluster analysis
Cluster analysis is a technique used to group objects into clusters based on similarities. There are several major approaches to cluster analysis including partitioning methods, hierarchy methods, density-based methods, and grid-based methods. Partitioning methods construct partitions of the data objects into a set number of clusters by optimizing a chosen criterion, such as k-means and k-medoids clustering algorithms.
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deep_Visualization in Data mining.ppt
The document discusses visual data mining techniques and their application to spatial data analysis. It proposes the CubeView system which uses a data cube structure to support data mining and visualization of large spatial datasets. CubeView allows selective visualization through spatial outlier detection algorithms that identify suspiciously deviating observations in the data. The system was applied to traffic data from road sensor networks to enable analysis of traffic patterns and outliers.
PPT s10-machine vision-s2
This document discusses unsupervised machine learning techniques for clustering unlabeled data. It covers k-means clustering, which partitions data into k groups based on minimizing distance between points and cluster centroids. It also discusses agglomerative hierarchical clustering, which successively merges clusters based on their distance. As an example, it shows hierarchical clustering of texture images from five classes to group similar textures.
ATTRIBUTE REDUCTION-BASED ENSEMBLE RULE CLASSIFIERS METHOD FOR DATASET CLASSI...
Attribute reduction and classification task are an essential process in dealing with large data
sets that comprise numerous number of input attributes. There are many search methods and
classifiers that have been used to find the optimal number of attributes. The aim of this paper is
to find the optimal set of attributes and improve the classification accuracy by adopting
ensemble rule classifiers method. Research process involves 2 phases; finding the optimal set of
attributes and ensemble classifiers method for classification task. Results are in terms of
percentage of accuracy and number of selected attributes and rules generated. 6 datasets were
used for the experiment. The final output is an optimal set of attributes with ensemble rule
classifiers method. The experimental results conducted on public real dataset demonstrate that
the ensemble rule classifiers methods consistently show improve classification accuracy on the
selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
Competition16
This document summarizes a machine learning project for an insurance company to predict customer purchasing behavior. It discusses:
- The objective is to predict the policy number and price a customer will purchase using historical customer data.
- The datasets include customer session and purchase histories. There is class imbalance with some policies having much more data than others.
- Data preprocessing included removing duplicates, outliers, and normalization. Feature selection used Pearson correlation to identify the most important features.
- SMOTE oversampling was used to address class imbalance for the policy number classification problem. Two models - decision forest and neural network - were evaluated for classification and regression.
- The decision forest model performed best for classification, while boosted decision
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Capter10 cluster basic : Han & Kamber
The document describes different types of clustering algorithms, including partitioning, hierarchical, density-based, and grid-based methods. Partitioning methods like k-means and k-medoids aim to partition objects into k clusters by optimizing an objective function. Hierarchical clustering builds a hierarchy of clusters based on distance, either through an agglomerative (bottom-up) or divisive (top-down) approach. Density-based methods identify clusters based on density rather than distance. Grid-based methods quantize the space into a finite number of cells that form a grid structure.
data mining.pptx
DCOM (Distributed Component Object Model) and CORBA (Common Object Request Broker Architecture) are two popular distributed object models. In this paper, we make architectural comparison of DCOM and CORBA at three different layers: basic programming architecture, remoting architecture, and the wire protocol architecture.
ClusetrigBasic.ppt
Cluster analysis is an unsupervised machine learning technique used to group unlabeled data points into clusters based on similarities. It involves finding groups of objects such that objects within a cluster are more similar to each other than objects in different clusters. The key goals of cluster analysis are to maximize intra-cluster similarity while minimizing inter-cluster similarity. Common applications of cluster analysis include market segmentation, document classification, and identifying homogeneous groups in biological data.
Lx3520322036
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
K- means clustering method based Data Mining of Network Shared Resources .pptx
K-means clustering is an unsupervised machine learning algorithm that is useful for clustering and categorizing unlabeled data points. It works by assigning data points to a set number of clusters, K, where each data point belongs to the cluster with the nearest mean. The document discusses how k-means clustering can be applied to network shared resources mining to overcome limitations of existing methods. It provides details on how k-means clustering works, compares it to other clustering algorithms, and demonstrates how it can accurately and efficiently cluster network resource data into groups within 0.6 seconds on average.
K- means clustering method based Data Mining of Network Shared Resources .pptx
K-means clustering is an unsupervised machine learning algorithm that is useful for clustering and categorizing unlabeled data points. It works by assigning data points to a set number of clusters, K, where each data point belongs to the cluster with the nearest mean. The document discusses how k-means clustering can be applied to network shared resources mining to overcome limitations of existing methods. It provides details on how k-means clustering works, compares it to other clustering algorithms, and demonstrates how it can accurately and efficiently cluster network resource data into groups within 0.6 seconds on average.
Scalable decision tree based on fuzzy partitioning and an incremental approach
Classification as a data mining materiel is the process of assigning entities to an already defined class by examining the features. The most significant feature of a decision tree as a classification method is its ability to data recursive partitioning. To choose the best attributes for partition, the value range of each continuous attribute should be divided into two or more intervals. Fuzzy partitioning can be used to reduce noise sensitivity and increase the stability of trees. Also, decision trees constructed with existing approaches, tend to be complex, and consequently are difficult to use in practical applications. In this article, a fuzzy decision tree has been introduced that tackles the problem of tree complexity and memory limitation by incrementally inserting data sets into the tree. Membership functions are generated automatically. Then fuzzy information gain is used as a fast-splitting attribute selection criterion and the expansion of a leaf is done attending only with the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm by reducing the complexity of the tree can overcome the memory limitation and make a balance between accuracy and complexity.
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Dissertation Data Fusion Summary Poster
- 1. MSc Dissertation 2013-14 MSc Course: KDD Using diversity for dynamic optimisation of data fusion ensembles Student: Chris Ballard Supervisor: Dr Wang School of Computing Sciences, August 2014 Aim and Objectives This study evaluated ensemble methods for the classification of heterogeneous data sources. In this context, its aims were to •Compare accuracy of feature-level fusion and existing ensemble approaches •Investigate how diversity can be used to optimise ensemble performance •Explore the link between diversity and accuracy Conclusions • Feature-level fusion outperformed decision-level fusion • Learn++ performed poorly with multiple datasets • DES significantly improved performance of Bagging when using decision-level fusion • DES reduced performance when using feature- level fusion • Weak positive correlation between ensemble accuracy and DES cluster diversity with CFD for some datasets. No relationship with MFD. Data The study used 9 benchmark datasets from the UCI Machine Learning Repository, partitioned into multiple feature sets to simulate different data sources. System Design • Base classifiers: Bagging – CART Decision Trees; Learn++ - Decision Stumps • Ensemble generation, DES and automated test rig implemented using Python 2.7.3 and scikit-learn • Simulated annealing used to optimise sub- ensemble diversity • Diversity measured using CFD, MFD and EEC diversity measures Experimental Results • Performance of Learn++.DES and Bagging.DES compared to benchmark methods using Friedman and Nemenyi tests (Figure 2) • Ensemble accuracy and CFD diversity by dataset (Figure 3); significant Spearman Correlation Coefficient between accuracy and CFD (p < 0.05) highlighted (Figure 4) Figure 2 - Critical difference diagram - Learn++.DES and Bagging.DES using decision-level fusion (CD=1.56, p=0.05) Methods • Two algorithms called Bagging.DES and Learn++.DES generate ensembles for each feature set: • Bagging and Learn++ used to generate pool of classifiers • Dynamic Ensemble Selection (DES) generates sub-ensembles for local regions of feature space with optimal diversity (Figure 1). • DES compared to AdaBoost, Bagging and Learn++ using feature- and decision- level fusion k-means clustering 1 2 3 4 5 6 N Classifiers Validation set Test instance 1 3 6 2 4 5 1 2 3 Cluster sub-ensemble Eck Decision combination Dynamic ensemble selection Ensemble decision Generated using Bagging or Learn++ ck Figure 1 – Dynamic Ensemble Selection in local clusters Figure 3 – Plots of ensemble accuracy and DES cluster CFD Correlation coefficient Arrhythmia 0.694 Bio Deg 0.401 Heart Disease 0.253 Hill Valley 0.168 Ionosphere 0.199 LSVT 0.46 Spectf 0.006 WDBC 0.574 Figure 4 – Spearman rank-order correlation coefficient