Jacek Biesiada1, Wlodzislaw Duch2,3,
1Division of Computer Studies, Department of Electrotechnology, The Silesian University of Technology, Katowice, Poland;
2School of Computer Engineering, Nanyang Technological University, Singapore,
3Department of Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland.
An algorithm for filtering information based on the Kolmogorov-Smirnov correlation- based approach has been implemented and tested on feature selection. The only parameter of this algorithm is statistical confidence level that two distributions are identical. Empirical comparisons with 4 other state-of-the-art features selection algorithms (FCBF, CorrSF, ReliefF and ConnSF) are very encouraging.
Reference: Biesiada J, Duch W (2005),
Feature Selection for High-Dimensional Data: A Kolmogorov-Smirnov Correlation-Based Filter.
Advances in Soft Computing, Springer Verlag, pp. 95-104, 2005.
Also: Computer Recognition Systems. Proc. of the 4th International Conference on Computer Recognition Systems (CORES'05), Ed. M. Kurzynski, E. Puchała, M. Wozniak, A. Zolnierek,
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