Completeness and consistency conditions for learning fuzzy rules

The completeness and consistency conditions were introduced in order to achieve acceptable concept recognition rules. In real problems, we can handle noise-affected examples and it is not always possible to maintain both conditions. Moreover, when we use fuzzy information there is a partial matching between examples and rules, therefore the consistency condition becomes a matter of degree. In this paper, a learning algorithm based on soft consistency and completeness conditions is proposed. This learning algorithm combines in a single process rule and feature selection and it is tested on different databases. (C) 1998 Elsevier Science B.V. All rights reserved.