Convex hull-based support vector machine rule extraction

Support vector machine (SVM) is a machine learning method based on statistical learning theory, and it can avoid the disadvantages well, such as over-training, weak normalization capability, etc. However, the black-box characteristic of SVM has limited its application. In order to open the black-box, a new rule extraction algorithm based on convex hull theory is proposed in this paper. First, all the vectors were clustered to be some clusters on the decision hyper-plane; then, extracted the convex hull for every cluster; finally, the region of each convex hull covered were transferred to each interval-type rule. Rule extraction has been experimented on two public datasets of Iris and Breast-cancer, which results showed that the proposed method can improve the accuracy of rule covering and fidelity.