Multiobjective Classification Rule Mining

Summary. In this chapter, we discuss the application of evolutionary multiob-jective optimization (EMO) to association rule mining. Especially, we focus our attention on classification rule mining in a continuous feature space where the an-tecedent and consequent parts of each rule are an interval vector and a class label, respectively. First we explain evolutionary multiobjective classification rule mining techniques. Those techniques are roughly categorized into two approaches. In one approach, each classification rule is handled as an individual. An EMO algorithm is used to search for Pareto-optimal rules with respect to some rule evaluation criteria such as support and confidence. In the other approach, each rule set is handled as an individual. An EMO algorithm is used to search for Pareto-optimal rule sets with respect to some rule set evaluation criteria such as accuracy and complexity. Next we explain evolutionary multiobjective rule selection as a post-processing pro-cedure in classification rule mining. Pareto-optimal rule sets are found from a large number of candidate classification rules, which are extracted from a database using an association rule mining technique. Then we examine the effectiveness of evolu-tionary multiobjective rule selection through computational experiments on some benchmark classification problems. Finally we examine the use of Pareto-optimal and near Pareto-optimal rules as candidate rules in evolutionary multiobjective rule selection.