Variable consistency bagging ensembles

Authors:
Jerzy Błaszczyński;Roman Słowiński;Jerzy Stefanowski
Affiliations:
Institute of Computing Science, Poznań University of Technology, Poznań, Poland;Institute of Computing Science, Poznań University of Technology, Poznań, Poland;Institute of Computing Science, Poznań University of Technology, Poznań, Poland
Venue:
Transactions on Rough Sets XI
Year:
2010

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Abstract

In this paper we claim that the classification performance of bagging classifier can be improved by drawing to bootstrap samples objects being more consistent with their assignment to decision classes. We propose a variable consistency generalization of the bagging scheme where such sampling is controlled by two types of measures of consistency: rough membership and monotonic ε measure. The usefulness of this proposal is experimentally confirmed with various rule and tree base classifiers. The results of experiments show that variable consistency bagging improves classification accuracy on inconsistent data.