Machine Learning
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Estimating missed actual positives using independent classifiers
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
On the Relationships Among Various Diversity Measures in Multiple Classifier Systems
ISPAN '08 Proceedings of the The International Symposium on Parallel Architectures, Algorithms, and Networks
Diversity in Combinations of Heterogeneous Classifiers
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An empirical study of applying ensembles of heterogeneous classifiers on imperfect data
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Improving bagging performance through multi-algorithm ensembles
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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Diversity plays an important role in the design of Multi-Classifier Systems, but its relationship to classification accuracy is still unclear from a theoretical perspective As a step towards the solution of this probelm, we take a different route and explore the relationship between diversity and correlation In this paper we provide a theoretical analysis and present a nonlinear function that relates diversity to correlation, which hence can be further related to accuracy This paper contributes to connecting existing research in diversity and correlation, and also providing a proxy to the relationship between diversity and accuracy Our experimental results reveal deeper insights into the role of diversity in Multi-Classifier Systems.