A Combination of Sample Subsets and Feature Subsets in One-Against-Other Classifiers

• Published in: Multiple Classifier Systems Vol. 4472; pp. 241 - 250
• Main Authors: Kudo, Mineichi, Shirai, Satoshi, Tenmoto, Hiroshi
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2007; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• ISBN: 9783540724810; 3540724818
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-72523-7_25

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.