A rough-fuzzy approach for generating classification rules

The generation of e1ective feature pattern-based classi$cation rules is essential to the development of any intelligent classi$er which is readily comprehensible to the user. This paper presents an approach that integrates a potentially powerful fuzzy rule induction algorithm with a rough set-assisted feature reduction method. The integrated rule generation mechanism maintains the underlying semantics of the feature set. Through the proposed integration, the original rule induction algorithm (or any other similar technique that generates descriptive fuzzy rules), which is sensitive to the dimensionality of the dataset, becomes usable on classifying patterns composed of a moderately large number of features. The resulting learned ruleset becomes manageable and may outperform rules learned using more features. This, as demonstrated with successful realistic applications, makes the present approach e1ective in handling real world problems. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.