FuzzyCN2: An algorithm for extracting fuzzy classification rule lists

Most of the algorithms for extracting fuzzy classification rules generate conjunctive antecedents that use all the attributes of the system. Using this kind of antecedents, the number of rules grows exponentially in terms of the number of attributes of the system. This paper presents a new algorithm, FuzzyCN2, for extracting conjunctive fuzzy classification rules. This algorithm is a fuzzy version of the well known CN2 algorithm and produces an ordered list of fuzzy rules. FuzzyCN2 generates antecedents that may not include all the attributes of the system. These antecedents may cover a wide number of instances and, so, the number of extracted rules is smaller. The algorithm introduces the use of linguistic hedges as part of the selectors, thus producing more compact rules and reducing the number of generated rules.