Application of a Rough Set-Based Inductive Learning System

Article type: Research Article

Authors: Hadjimichael, Michael^a | Wasilewska, Anita^b

Affiliations: [a] Department of Computer Science, University of Regina, Regina, SK, Canada, S4S 0A2 | [b] Department of Computer Science, State University of New York at Stony Brook, USA 11794

Abstract: We present here an application of Rough Set formalism to Machine Learning. The resulting Inductive Learning algorithm is described, and its application to a set of real data is examined. The data consists of a survey of voter preferences taken during the 1988 presidential election in the U.S.A. Results include an analysis of the predictive accuracy of the generated rules, and an analysis of the semantic content of the rules.

DOI: 10.3233/FI-1993-182-409

Journal: Fundamenta Informaticae, vol. 18, no. 2-4, pp. 209-220, 1993