BETTER ALTERNATIVES FOR STEPWISE  DISCRIMINANT ANALYSIS

Katarzyna Stąpor

doi:10.18778/0208-6018.311.02

Authors

Katarzyna Stąpor Silesian Technical University Gliwice

DOI:

https://doi.org/10.18778/0208-6018.311.02

Keywords:

discriminant analysis, stepwise procedures, feature selection, metaheuristic, tabu search

Abstract

Discriminant Analysis can best be defined as a technique which allows the classification of an individual into several dictinctive populations on the basis of a set of measurements. Stepwise discriminant analysis (SDA) is concerned with selecting the most important variables whilst retaining the highest discrimination power possible. The process of selecting a smaller number of variables is often necessary for a variety number of reasons. In the existing statistical software packages SDA is based on the classic feature selection methods. Many problems with such stepwise procedures have been identified. In this work the new method based on the metaheuristic strategy tabu search will be presented together with the experimental results conducted on the selected benchmark datasets. The results are promising.

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Author Biography

Katarzyna Stąpor, Silesian Technical University Gliwice

Institute of Computer Science

professor

References

Blum Ch., Roli A. (2003), Metaheuristics in combinatorial optimization: overview and conceptual comparison, ACM Computing Surveys, vol. 35, 3, p. 268-308.
Google Scholar

Glover F. (1989), Tabu Search. Part I, ORSA Journal of Computing, v.1, p. 190-206.
Google Scholar

Huberty C.J. (1989), Problems with stepwise methods – better alternatives, in: Thompson B. (ed.) Advances in Social Science Methodology, vol.1, 43-70, Greenwich, CI: JAI Press.
Google Scholar

Kohavi R., John G. (1997), Wrappers for feature subset selection, Artificial Intelligence, vol. 97, 1-2, p. 234-273.
Google Scholar

Krzyśko M. (1990), Discriminant analysis, WNT, Warszawa (in Polish).
Google Scholar

Murphy P.M., Aha D.W. (1994), UCI repository of machine learning. University of California, Department of Information and Computer Science, http://www.ics.uci.edu/-~mlearn/MLRepository.html.
Google Scholar

Pacheco J. et al. (2006), Analysis of new variable selection methods in discriminant analysis, Computational Statistics&Data Analysis, vol.51, 3, p. 1463-1478.
Google Scholar

STATISTICA – package documentation, (2005), StatSoft Inc.
Google Scholar

“Author” (2011).
Google Scholar

Zhang H, Sun G. (2002), Feature selection using tabu search method, Pattern Recognition, 35, p. 701-711.
Google Scholar