Minimum class variance support vector ordinal regression

作者：Wang, Xiaoming^*; Hu, Jinrong; Huang, Zengxi

来源：International Journal of Machine Learning and Cybernetics, 2017, 8(6): 2025-2034.

DOI：10.1007/s13042-016-0582-3

• Machine learning
• Ordinal regression
• Support vector machine
• Support vector ordinal regression

The support vector ordinal regression (SVOR) method is derived from support vector machine and developed to tackle the ordinal regression problems. However, it ignores the distribution characteristics of the data. In this paper, we propose a novel method to handle the ordinal regression problems. This method is referred to as minimum class variance support vector ordinal regression (MCVSVOR). In contrast with SVOR, MCVSVOR explicitly takes into account the distribution of the categories and achieves better generalization performance. Moreover, the problem of MCVSVOR can be transformed into one of SVOR. Thus, the existing software of SVOR can be used to solve the problem of MCVSVOR. In the paper, we first discuss the linear case of MCVSVOR and then develop the nonlinear MCVSVOR through using the kernelization trick. The comprehensive experiment results show that the proposed method is effective and can achieve better generalization performance in contrast with SVOR.

• 出版日期2017-12
• 单位成都信息工程大学; 西华大学