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.
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© 2007 Springer Berlin Heidelberg
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Kudo, M., Shirai, S., Tenmoto, H. (2007). A Combination of Sample Subsets and Feature Subsets in One-Against-Other Classifiers. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_25
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DOI: https://doi.org/10.1007/978-3-540-72523-7_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72481-0
Online ISBN: 978-3-540-72523-7
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