Construction and application of pre-classified smooth semi-supervised twin support vector machine

In order to handle the semi-supervised problem quickly and efficiently in the twin support vector machine (TWSVM) field, a semi-supervised twin support vector machine (S²TSVM) is proposed by adding the original unlabeled samples. In S²TSVM, the addition of unlabeled samples can easily cause the classification hyper plane to deviate from the sample points. Then a center-distance principle is proposed to pre-classify unlabeled samples, and a pre-classified S²TSVM (PS²TSVM) is proposed. Compared with S²TSVM, PS²TSVM not only improves the problem of the samples deviating from the classification hyper plane, but also improves the training speed. Then PS²TSVM is smoothed. After smoothing the model, the pre-classified smooth S²TSVM (PS³TSVM) is obtained, and its convergence is deduced. Finally, nine datasets are selected in the UCI machine learning database for comparison with other types of semi-supervised models. The experimental results show that the proposed PS³TSVM model has better classification results.