Approximate Entropy Reducts

Article type: Research Article

Authors: Ślezak, Dominik^;

Affiliations: Department of Computer Science, University of Regina, Regina, SK, S4S 0A2, Canada | Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland

Abstract: We use information entropy measure to extend the rough set based notion of a reduct. We introduce the Approximate Entropy Reduction Principle (AERP). It states that any simplification (reduction of attributes) in the decision model, which approximately preserves its conditional entropy (the measure of inconsistency of defining decision by conditional attributes) should be performed to decrease its prior entropy (the measure of the model's complexity). We show NP-hardness of optimization tasks concerning application of various modifications of AERP to data analysis.

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Journal: Fundamenta Informaticae, vol. 53, no. 3-4, pp. 365-390, 2002