Affiliations: Postgraduate School of Molecular Medicine, Warsaw, Poland. [email protected] | Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden. [email protected] | Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland. [email protected] | Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland. [email protected] | Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland. [email protected] | Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, Poland. [email protected]
Note: [] Address for correspondence: Uppsala Biomedicinska Centrum BMC, Husarg. 3, Box 596, 751 24 Uppsala, Sweden Also works: Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
Abstract: An important step prior to constructing a classifier for a very large data set is feature selection. With many problems it is possible to find a subset of attributes that have the same discriminative power as the full data set. There are many feature selection methods but in none of them are Rough Set models tied up with statistical argumentation. Moreover, known methods of feature selection usually discard shadowed features, i.e. those carrying the same or partially the same information as the selected features. In this study we present Random Reducts (RR) - a feature selection method which precedes classification per se. The method is based on the Monte Carlo Feature Selection (MCFS) layout and uses Rough Set Theory in the feature selection process. On synthetic data, we demonstrate that the method is able to select otherwise shadowed features of which the user should be made aware, and to find interactions in the data set.
Keywords: Feature selection, random reducts, rough sets, Monte Carlo
