A combination of sample subsets and feature subsets in one-against-other classifiers

Authors:
Mineichi Kudo;Satoshi Shirai;Hiroshi Tenmoto
Affiliations:
Division of Computer Science, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan;Division of Computer Science, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan;Department of Information Engineering, Kushiro National College of Technology, Kushiro, Hokkaido, Japan
Venue:
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Year:
2007

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Abstract

We investigated a "sample-feature-subset" approach which is a kind of extension of bagging and the random subspace method. In the procedure, we collect some subsets of training samples in each class and then remove the redundant features from those subsets. As a result, those subsets are represented in different feature spaces. We constructed one-against-other classifiers as the component classifiers by feeding those subsets to a base classifier and then combined them in majority voting. Some experimental results showed that this approach outperformed the random subspace method.