Elements of information theory
Elements of information theory
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
The multiinformation function as a tool for measuring stachastic dependence
Learning in graphical models
Multivariate information bottleneck
Neural Computation
An Information Theoretic Perspective on Multiple Classifier Systems
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Information theoretical analysis of multivariate correlation
IBM Journal of Research and Development
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
The impact of diversity on the accuracy of evidential classifier ensembles
International Journal of Approximate Reasoning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Diversity regularized ensemble pruning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Understanding ensemble diversity is one of the most important fundamental issues in ensemble learning. Inspired by a recent work trying to explain ensemble diversity from the information theoretic perspective, in this paper we study the ensemble diversity from the view of multi-information. We show that from this view, the ensemble diversity can be decomposed over the component classifiers constituting the ensemble. Based on this formulation, an approximation is given for estimating the diversity in practice. Experimental results show that our formulation and approximation are promising.