Abstract
In recent years, data mining techniques have been widely used to solve classification and prediction problems. Although employing the data mining methodology can unearth information that people are interested in, it has faced numerous challenges, including issues regarding class imbalance, large data volume, continuous and discrete data, and lack of explanatory abilities. These issues can lead to time-consuming computation and lower classification and prediction performance. Support vector machines (SVM) are the most popular classifier with optimal mathematical theoretical foundation. However, the disadvantage of SVM is that representation of decision boundaries are complex mathematical formulation, which increase the difficulty for people to comprehend their implications. Therefore, it is desired to construct a rule extraction algorithm from SVM to enhance their explanatory abilities. The study proposed an integration of SVM and gene expression programming (GEP), used support vectors (SVs) on the decision boundaries of SVM as the possible solutions space of GEP chromosome to extract rules for enhancing explanatory abilities of the black box model of SVM. Moreover, three performance metrics, including accuracy, sensitivity, and specificity, were adopted to evaluate the classification performances of the proposed method and other classifiers. For UCI datasets, the results showed that the classification performances of SVM + GEP were better than that of the other three, which verified that the method had an excellent classification property. From the case study, the proposed method not only greatly outperformed the other rule learners on classification performance but also selected important attributes as strategic references for management decision making. The proposed SVM + GEP has been successfully applied to UCI data collection and actual cases.