Classification rule mining approach based on multiobjective optimization

In this paper, a novel approach for classification rule mining is presented. The remarkable relationship between the rule extraction procedure and the concept of multiobjective optimization is emphasized. The range values of features composing the rules are handled as decision variables in the modelled multiobjective optimization problem. The proposed method is applied to three well-known datasets in literature. These are Iris, Haberman's Survival Data and Pima Indians Diabetes Datasets obtained from machine learning repository of University of California at Irvine (UCI). The classification rules are extracted with 100% accuracy for all datasets. These experimental results are the best outcomes found in literature so far.