A Novel Fuzzy Associative Classifier Based on Information Gain and Rule-Covering

Fuzzy Associative Classification has attracted remarkable research attention for knowledge discovery and business analytics in recent years due to its merits in accuracy and linguistic modeling. Furthermore, it is deemed meaningful to construct an associative classifier with a compact set of rules (i.e., compactness), which is easy to understand and use in decision making. This paper introduces a novel fuzzy associative classification approach called GFRC (i.e., Gain-based Fuzzy Rule-Covering classification). Two desirable strategies are developed in GFRC so as to enhance the compactness with accuracy. One strategy is fuzzy partitioning for data discretization, in that simulated annealing is incorporated based on the information entropy measure; the other strategy is a data-redundancy resolution coupled with the rule-covering treatment. Moreover, data experiments show that GFRC had good accuracy, and was significantly advantageous over other classifiers in compactness.