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Fuzzy Discretization of Feature Space for a Rough Set Classifier by Amitava Roy; Sankar K Pal is a Computer Science article available to read on EtoBox.

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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.

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Author

Amitava Roy; Sankar K Pal

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0167-8655)

Published

2003

Language

EN

Field

Computer Science (Physical Sciences)