A Least Squares Twin Support Vector Machine Method with Uncertain Data

Twin support vector machine learns two nonparallel hyperplanes by calculating two quadratic programming problems to solve the binary classification problems. However, in practical applications, the data usually contain uncertain information, making it difficult to construct the classification model. This paper proposed a new and efficient uncertain-data-based least squares twin support vector machine (ULSTSVM) method to address the problem of data uncertainty. Firstly, since the data may contain uncertain information, a noise vector was introduced to model the uncertain information of each example. Secondly, the noise vectors were incorporated into the least squares TWSVM. Finally, to solve the derived learning problem, we employed a two-step heuristic framework to train the least squares TWSVM classifier and updated the noise vectors alternatively. The experiments showed that our proposed ULSTSVM outperforms the baselines in training time and meanwhile achieves comparable classification accuracy. In sum, ULSTSVM adopts a noise vector to model the uncertain information and transforms the quadratic programming problems of TWSVM into linear equations, such that better classification accuracy and higher training efficiency can be obtained.