Learning a coverage set of maximally general fuzzy rules by rough sets

Expert systems have been widely used in domains where mathematical models cannot be easily built, human experts are not available or the cost of querying an expert is high. Machine learning or data mining can extract desirable knowledge or interesting patterns from existing databases and ease the development bottleneck in building expert systems. In the past we proposed a method [Hong, T.P., Wang, T.T., Wang, S.L. (2000). Knowledge acquisition from quantitative data using the rough-set theory. Intelligent Data Analysis (in press).], which combined the rough set theory and the fuzzy set theory to produce all possible fuzzy rules from quantitative data. In this paper, we propose a new algorithm to deal with the problem of producing a set of maximally general fuzzy rules for coverage of training examples from quantitative data. A rule is maximally general if no other rule exists that is both more general and with larger confidence than it. The proposed method first transforms each quantitative value into a fuzzy set of linguistic terms using membership functions and then calculates the fuzzy lower approximations and the fuzzy upper approximations. The maximally general fuzzy rules are then generated based on these fuzzy approximations by an iterative induction process. The rules derived. can then be used to build a prototype knowledge base in a fuzzy expert system. (C) 2000 Elsevier Science Ltd. All rights reserved.