Minimum class variance support vector ordinal regression

doi:10.1007/s13042-016-0582-3

Minimum class variance support vector ordinal regression

2016-08-18

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DOI：10.1007/s13042-016-0582-3

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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.

Keyword:

Machine learning

Ordinal regression

Support vector machine

Support vector ordinal regression

Minimum class variance support vector ordinal regression

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