An improved fuzzy twin support vector machine

ABSTRACT Fuzzy twin support vector machine is an important machine learning method and it overcomes impact of noise and outlier data on classification. However, this method still accomplishes minimization of empirical risk such that overfitting is easily produced in the process of training. For solving them, a novel fuzzy twin support vector machine model is presented by introducing regularized item. Classifier is obtained by using quadratic programming and over-relaxation method to solve the model. Some UCI datasets are selected to conduct the experiments and validate the effectiveness of the proposed method.