Abstract
In this paper we compare recently developed and highly effective sequential feature selection algorithms with approaches based on evolutionary algorithms enabling parallel feature subset selection. We introduce the oscillating search method, employ permutation encoding offering some advantages over the more traditional bitmap encoding for the evolutionary search, and compare these algorithms to the often studied and well-performing sequential forward floating search. For the empirical analysis of these algorithms we utilize three well-known benchmark problems, and assess the quality of feature subsets by means of the statistical Bhattacharyya distance measure.
This work has been supported by AKTION Österreich Tschechische Republik under grant AKTION 23p20: Comparison of statistical and evolutionary approaches to feature selection in pattern recognition“
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Keywords
- Feature Selection
- Evolutionary Algorithm
- Feature Subset
- Feature Selection Method
- Feature Selection Algorithm
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Mayer, H.A., Somol, P., Huber, R., Pudil, P. (2000). Improving Statistical Measures of Feature Subsets by Conventional and Evolutionary Approaches. In: Ferri, F.J., Iñesta, J.M., Amin, A., Pudil, P. (eds) Advances in Pattern Recognition. SSPR /SPR 2000. Lecture Notes in Computer Science, vol 1876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44522-6_8
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DOI: https://doi.org/10.1007/3-540-44522-6_8
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