Earticle

초록

영어

Conventional Support Vector Machines (SVMs) have been utilized as classifiers for binary classification problems. However, certain real world problems, including corporate bond rating, cannot be addressed by binary classifiers because these are multi-class problems. For this reason, numerous studies have attempted to transform the original SVM into a multiclass classifier. These studies, however, have only considered nominal classification problems. Thus, these approaches have been limited by the existence of multiclass classification problems where classes are not nominal but ordinal in real world, such as corporate bond rating and multiclass customer classification. In this study, we adopt a novel multiclass SVM which can address ordinal classification problems using ordinal pairwise partitioning (OPP). The proposed model in our study may use fewer classifiers, but it classifies more accurately because it considers the characteristics of the order of the classes. Although it can be applied to all kinds of ordinal multiclass classification problems, most prior studies have applied it to finance area like bond rating. Thus, this study applies it to a real world customer level classification case for implementing customer relationship management. The result shows that the ordinal multiclass SVM model may also be effective for customer level classification.

목차

Abstract
 Ⅰ. Introduction
 Ⅱ. Prior Studies
  2.1 Prior Studies on Customer Classification and Response Modeling
  2.2 Conventional Support Vector Machines
  2.3 Multiclass Support Vector Machines
  2.4 Ordinal Multiclass SVMs
 Ⅲ. Experimental Design and Results
  3.1 Research Data
  3.2 Experimental Design
  3.3 Experimental Results
 Ⅳ. Conclusions
 

저자

• Kyoung-jae Kim [ Department of Management Information Systems, Dongguk University-Seoul ] Main author
• Hyunchul Ahn [ School of Management Information Systems, Kookmin University ] Corresponding author

참고문헌