Discovering fuzzy classification rules using Genetic Network Programming

Classification rule mining is an active data mining research area. Most related studies have shown how binary valued datasets are handled. However, datasets in real-world applications, usually consist of fuzzy and quantitative values. As a result, the idea to combine the different approaches with fuzzy set theory has been applied more frequently in recent years. Fuzzy sets can help to overcome the so-called sharp boundary problem by allowing partial memberships to the different sets, not only 1 and 0. On the other hand, fuzzy sets theory has been shown to be a very useful tool because the mined rules are expressed in linguistic terms, which are more natural and understandable for human beings. This paper proposes the combination of fuzzy set theory and ldquogenetic network programmingrdquo (GNP) for discovering fuzzy classification rules from given quantitative data. GNP, as an extension of genetic algorithms (GA) and genetic programming (GP), is an evolutionary optimization technique that uses directed graph structures as genes instead of strings and trees; this feature contributes creating quite compact programs and implicitly memorizing past action sequences. At last, experimental results conducted on a real world database verify the performance of the proposed method.