Conceptual Modeling of Coincident Failures in Multiversion Software
IEEE Transactions on Software Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Theoretical Basis for the Analysis of Multiversion Software Subject to Coincident Errors
IEEE Transactions on Software Engineering
A framework for the analysis of majority voting
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
On diversity and accuracy of homogeneous and heterogeneous ensembles
International Journal of Hybrid Intelligent Systems
Influence of Resampling and Weighting on Diversity and Accuracy of Classifier Ensembles
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Classifier Ensemble Generation for the Majority Vote Rule
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
On the diversity-performance relationship for majority voting in classifier ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
A metric for unsupervised metalearning
Intelligent Data Analysis
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Diversity is an important consideration in classifier ensembles, it can be potentially expolited in order to obtain a higher classification accuracy. There is no widely accepted formal definition of diversity in classifier ensembles, thus making an objective evaluation of diversity measures difficult. We propose a set of properties and a linear program based framework for the analysis of diversity measures for ensembles of binary classifiers. Although we regard the question of what exactly defines diversity in a classifier ensemble as open, we show that the framework can be used effectively to evaluate diversity measures. We explore whether there is a useful relationship between the selected diversity measures and the ensemble accuracy. Our results cast doubt on the usefulness of diversity measures in designing a classifier ensemble, although the motivation for enforcing diversity in a classifier ensemble is justified.