Fuzzy discretization of feature space for a rough set classifier

A concept of fuzzy discretization of feature space for a rough set theoretic classifier is explained. Fuzzy discretization is characterised by membership value, group number and affinity corresponding to an attribute value, unlike crisp discretization which is characterised only by the group number. The merit of this approach over both crisp discretization in terms of classification accuracy, is demonstrated experimentally when overlapping data sets are used as input to a rough set classifier. The effectiveness of the proposed method has also been observed in a multi-layer perceptron in which case raw (non-discretized) data is considered as input, in addition to discretized ones. (C) 2002 Elsevier Science B.V. All rights reserved.