Relevance and Redundancy Analysis for Ensemble Classifiers

Authors:
Rakkrit Duangsoithong;Terry Windeatt
Affiliations:
Center for Vision, Speech and Signal Processing, University of Surrey, Guildford, United Kingdom GU2 7XH;Center for Vision, Speech and Signal Processing, University of Surrey, Guildford, United Kingdom GU2 7XH
Venue:
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2009

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Abstract

In machine learning systems, especially in medical applications, clinical datasets usually contain high dimensional feature spaces with relatively few samples that lead to poor classifier performance. To overcome this problem, feature selection and ensemble classification are applied in order to improve accuracy and stability. This research presents an analysis of the effect of removing irrelevant and redundant features with ensemble classifiers using five datasets and compared with floating search method. Eliminating redundant features provides better accuracy and computational time than removing irrelevant features of the ensemble.