Abstract
For two-class problems we propose two feature selection criteria based on kernel discriminant analysis. The first one is the objective function of kernel discriminant analysis (KDA) and the second one is the KDA-based exception ratio. We show that the objective function of KDA is monotonic for the deletion of features, which ensures stable feature selection. The KDA-based exception ratio defines the overlap between classes in the one-dimensional space obtained by KDA. The computer experiments show that the both criteria work well to select features but the former is more stable.
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Ashihara, M., Abe, S. (2006). Feature Selection Based on Kernel Discriminant Analysis. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_29
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DOI: https://doi.org/10.1007/11840930_29
Publisher Name: Springer, Berlin, Heidelberg
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