Feature Selection Based on the Rough Set Theory and Expectation-Maximization Clustering Algorithm

Authors:
Farideh Fazayeli;Lipo Wang;Jacek Mandziuk
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798;Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland 00-661
Venue:
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Year:
2008

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Abstract

We study the Rough Set theory as a method of feature selection based on tolerant classes that extends the existing equivalent classes. The determination of initial tolerant classes is a challenging and important task for accurate feature selection and classification. In this paper the Expectation-Maximization clustering algorithm is applied to determine similar objects. This method generates fewer features with either a higher or the same accuracy compared with two existing methods, i.e., Fuzzy Rough Feature Selection and Tolerance-based Feature Selection, on a number of benchmarks from the UCI repository.