Jacek Biesiada1 and Wlodzislaw Duch2.
1Division of Computer Studies, Department of Electrotechnology, The Silesian University of Technology, Katowice, Poland.
2Department of Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland.
A filter algorithm using F-measure has been used with feature redundancy removal based on the Kolmogorov-Smirnov (KS) test for rough equality of statistical distributions. As a result computationally efficient K-S Correlation-Based Selection algorithm has been developed and tested on three high-dimensional microarray datasets using four types of classifiers. Results are quite encouraging and several improvements are suggested.
Preprint for comments in PDF, 96 KB.
Reference: Biesiada J, Duch W,
A Kolmogorov-Smirnov Correlation-Based Filter for Microarray Data.
Lecture Notes in Computer Science, Vol. 4985, pp. 285–294, 2008.
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