A novel business analytics approach and case study - fuzzy associative classifier based on information gain and rule-covering

Associative classification has attracted remarkable research attention for business analytics in recent years due to its merits in accuracy and understandability. 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 presents a novel approach to fuzzy associative classification (namely Gain-based Fuzzy Rule-Covering classification, GFRC), which is a fuzzy extension of an effective classifier GARC. In GFRC, two desirable strategies are introduced to enhance the compactness with accuracy. One strategy is fuzzy partitioning for data discretization to cope with the 'sharp boundary problem', in that simulated annealing is incorporated based on the information entropy measure; the other strategy is a data-redundancy resolution coupled with the rulecovering treatment. Data experiments show that GFRC had good accuracy, and was significantly advantageous over other classifiers in compactness. Moreover, GFRC is applied to a real-world case for predicting the growth of sellers in an electronic marketplace, illustrating the classification effectiveness with linguistic rules in business decision support.