Twin SVM for conditional probability estimation in binary and multiclass classification. (April 2023)

• Record Type: Journal Article
• Authors: Shao, Yuan-Hai; Lv, Xiao-Jing; Huang, Ling-Wei; Bai, Lan
• Is Part Of: Pattern recognition. Volume 136(2023)
• Publication Date: 2023-04
• Subjects: Support vector machine -- Twin support vector machines -- Conditional probability -- Binary classification -- Multiclass classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.109253
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Conditional probability twin SVM (CPTWSVM) is presented to estimate the conditional probability function and the empirical risk of each class. CPTWSVM not only can measure the misclassification of the training samples for each class, but also returns the discriminant projections and distribution function of each class. CPTWSVM can eliminate the problems of inconsistent measurement in TWSVMs. CPTWSVM can be extended to multiclass classification and maintain the above properties. Experiments show that CPTWSVM has a better performance than some leading SVMs and TWSVMs communities. Abstract: In this paper, we estimate the conditional probability function by presenting a new twin SVM model (CPTWSVM) in binary and multiclass classification problems. The motivation of CPTWSVM is to implement the empirical risk minimization on training data, which is hard to realize in traditional twin SVMs. In each subproblem of CPTWSVM, it measures the empirical risk and outputs the corresponding probability estimate of each class, which eliminates the problems of inconsistent measurement in twin SVMs. Though an additional discriminant objective function is introduced, the optimization problem size of each subproblem is smaller than conditional probability SVM, and is solved by block decomposition algorithm efficiently. In addition, we extend CPTWSVM to multiclass classification by estimating the conditional probability of each class, and maintaining the above properties. Numerical experiments on benchmark and real application datasets demonstrate that CPTWSVM outputs the estimate of probability and the data projection well, resulting in better generalization ability than some leading TWSVMs communities, in terms of binary and multiclass classification.