Probabilistic outputs for twin support vector machines

Authors:
Yuan-Hai Shao;Nai-Yang Deng;Zhi-Min Yang;Wei-Jie Chen;Zhen Wang
Affiliations:
Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, PR China;College of Science, China Agricultural University, Beijing 100083, PR China;Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, PR China;Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, PR China;College of Mathematics, Jilin University, Changchun 130012, PR China
Venue:
Knowledge-Based Systems
Year:
2012

Citing 31
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Abstract

In many cases, the output of a classifier should be a calibrated posterior probability to enable post-processing. However, twin support vector machines (TWSVM) do not provide such probabilities. In this paper, we propose a TWSVM probability model, called PTWSVM, to estimate the posterior probability. Note that our model is quite different from the SVM probability model because of the different mechanism of TWSVM and SVM. In fact, for TWSVM, we first define a new ranking continues output by comparing the distances between the sample and the two non-parallel hyperplanes, and then map this ranking continues output into probability by training the parameters of an additional sigmoid function. Our PTWSVM has been tested on both artificial datasets and several data-mining-style datasets, and the numerical experiments indicate that PTWSVM yields nice results.