Twin support vector machine obtains a pair of non-parallel hyperplanes by solving two smaller quadratic programming problems, which improves the performance of the classifier in terms of time and accuracy. Because this method uses the Hinge loss function, the twin support vector function is more sensitive to noise and the resampling is unstable. Therefore, in view of the multi-classification problem, the pinball loss function and weights of samples are introduced into the twin support vector machine, and the binary classifiers obtained are combined by a one-versus-one method,and a one-versus-one weighted twin support vector machine based on pinball loss is proposed. It solves the problems of the sensitivity of twin support vector machines to noise and the instability of resampling. In addition, for the different effects of samples, some methods for calculating the weights of samples are given. In the experiment, standard data sets and artificial synthetic data sets are selected to verify the proposed algorithm Pin-OVO-TWSVM and compared the performance with OVO-TWSVM, OVA-TWSVM and Pin-OVO-TWSVM to show the effectiveness of the proposed method.
LI Kai
,
LI Jie
. One-versus-one weighted twin support vector machine with pinball loss function[J]. Journal of Hebei University(Natural Science Edition), 2020
, 40(6)
: 647
-656
.
DOI: 10.3969/j.issn.1000-1565.2020.06.013
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