Abstract
The effectiveness of classifier-independent feature selection is described. The aim is to remove garbage features and to improve the classification accuracy of all the practical classifiers compared with the situation where all the given features are used. Two algorithms of classifier-independent feature selection and two other conventional classifier-specific algorithms are compared on three sets of real data. In addition, two-stage feature selection is proposed.
Cite
CITATION STYLE
Kudo, M., & Sklansky, J. (1998). Classifier-independent feature selection for two-stage feature selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1451, pp. 548–554). Springer Verlag. https://doi.org/10.1007/bfb0033278
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