earticle

영어

Support vector machines (SVMs) were originally designed for binary classification problems. However, some real-world multiclass problems including corporate bond rating can’t be solved by the binary classifier. For this reason, many researchers have tried to extend original SVM to multiclass classification. However, their studies have only focused on classifying samples into nominal categories. But, their approaches also have a limitation because there are multiclass classification problems whose classes are ordinal in real world. In this study, we propose ordinal multiclass SVMs which apply ordinal pairwise partitioning (OPP) to conventional SVMs in order to handle ordinal multiple classes efficiently and effectively. Our suggested model may use less classifier but predict more accurately because it utilizes characteristics of the order of the classes. We apply it to the real-world customer classification case for validating the proposed model. The result shows that the proposed multiclass SVM models improve classification performance.