基于数据分布特征的线性孪生支持向量机

Abstract

Abstract: Twin Support Vector Machine(TWSVM) have been successfully applied in many fields.However,the standard TWSVM model have poor robustness when dealing with data classification problems involving distribution characteristics,especially when uncertainty in data fluctuates wildly,the standard classification model,which doesn’t consider the distribution characteristics,is no longer satisfactory for classification accuracy.Therefore,a weighted linear twin support vector machine model based on data distribution characteristics was proposed in this paper.The new model,denoted by TWSVM-U,further considers the influence of data distribution characteristics on the locations of classification hyperplanes,and constructs distance weights quantitatively according to data dispersity at the normal vector directions of classification hyperplanes.TWSVM-U is a generalization of TWSVM.In fact,when training samples do not have distribution characteristics,TWSVM-U model will degenerate to the standard TWSVM model.Experiments with 10-fold cross validation show that the TWSVM-U model performs better than the SVM and the TWSVM on classification problems with large data fluctuation range.

Key words: Binary classification, Twin support vector machine, Uncertain information, Weighted distance

CLC Number:

TP301

SONG Rui-yang, MENG Hua, LONG Zhi-guo. Linear Twin Support Vector Machine Based on Data Distribution Characteristics[J].Computer Science, 2019, 46(6A): 407-411.

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