Floating search methods in feature selection
Pattern Recognition Letters
Divergence Based Feature Selection for Multimodal Class Densities
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Novel Methods for Subset Selection with Respect to Problem Knowledge
IEEE Intelligent Systems
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PRIS '01 Proceedings of the 1st International Workshop on Pattern Recognition in Information Systems: In conjunction with ICEIS 2001
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Pattern Recognition Letters
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Expert Systems with Applications: An International Journal
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ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
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Recent advances in the statistical methodology for selecting optimal subsets of features for data representation and classification are presented. The paper attempts to provide a guideline which approach to choose with respect to the extent of a priori knowledge of the problem. Two basic approaches are reviewed and the conditions under which they should be used are specified. One approach involves the use of the computationally effective floating search methods. The alternative approach trades off the requirement for a priori information for the requirement of sufficient data to represent the distributions involved. Owing to its nature it is particularly suitable for cases when the underlying probability distributions are not unimodal. The approach attempts to achieve simultaneous feature selection and decision rule inference. According to the criterion adopted there are two variants allowing the selection of features either for optimal representation or discrimination. A consulting system aimed to guide a user to choose a proper method for the problem at hand is being prepared.