The nature of statistical learning theory
The nature of statistical learning theory
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Improved Generalization Through Explicit Optimization of Margins
Machine Learning
Machine Learning
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Hierarchical overlapped SOM's for pattern classification
IEEE Transactions on Neural Networks
Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
DILS '08 Proceedings of the 5th international workshop on Data Integration in the Life Sciences
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Threshold-optimized decision-level fusion and its application to biometrics
Pattern Recognition
Negative correlation in incremental learning
Natural Computing: an international journal
True Path Rule Hierarchical Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Predicting protein subcellular locations for Gram-negative bacteria using neural networks ensemble
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
The diversity/accuracy dilemma: an empirical analysis in the context of heterogeneous ensembles
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Averaged Naive Bayes Trees: A New Extension of AODE
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Feature selection in heterogeneous structure of ensembles: a genetic algorithm approach
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Stability analysis on rough set based feature evaluation
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Information theoretic combination of pattern classifiers
Pattern Recognition
Sparse ensembles using weighted combination methods based on linear programming
Pattern Recognition
Non-uniform layered clustering for ensemble classifier generation and optimality
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Ectropy of diversity measures for populations in Euclidean space
Information Sciences: an International Journal
Ensembles of ARTMAP-based neural networks: an experimental study
Applied Intelligence
The design of evolutionary multiple classifier system for the classification of microarray data
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Ensemble of classifiers based on hard instances
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Analyzing the relationship between diversity and evidential fusion accuracy
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
The impact of diversity on the accuracy of evidential classifier ensembles
International Journal of Approximate Reasoning
Margin optimization based pruning for random forest
Neurocomputing
Diversity regularized ensemble pruning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Classifier ensemble using a heuristic learning with sparsity and diversity
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
Ensemble classifier generation using non-uniform layered clustering and Genetic Algorithm
Knowledge-Based Systems
A survey of multiple classifier systems as hybrid systems
Information Fusion
A metric for unsupervised metalearning
Intelligent Data Analysis
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Diversity among the base classifiers is deemed to be important when constructing a classifier ensemble. Numerous algorithms have been proposed to construct a good classifier ensemble by seeking both the accuracy of the base classifiers and the diversity among them. However, there is no generally accepted definition of diversity, and measuring the diversity explicitly is very difficult. Although researchers have designed several experimental studies to compare different diversity measures, usually confusing results were observed. In this paper, we present a theoretical analysis on six existing diversity measures (namely disagreement measure, double fault measure, KW variance, inter-rater agreement, generalized diversity and measure of difficulty), show underlying relationships between them, and relate them to the concept of margin, which is more explicitly related to the success of ensemble learning algorithms. We illustrate why confusing experimental results were observed and show that the discussed diversity measures are naturally ineffective. Our analysis provides a deeper understanding of the concept of diversity, and hence can help design better ensemble learning algorithms.