Structural Fuzzy Multi-class Support Vector Machine

Abstract

Support vector machine is a kind of generalized linear classifier that classifies data in a supervised learning manner, and its decision boundary is the maximum margin hyperplane for solving learning samples. It is used to solve the problem of binary classification. For multi-classification problems, a combination method of multiple binary classifiers is usually used to solve, but such methods are prone to generate inseparable regions of data. Therefore, on the basis of constructing a multi-class problem directly, using the pinball loss function, and introducing the structural information of different classes in the data and the role of different samples, a new support vector machine algorithm Pin-SFSimMSVM for solving multi-classification problems is proposed. It not only retains the advantages of avoiding the existence of inseparable regions and fast calculation speed of multiple types of data, but also is insensitive to noise and resampling data, and has greatly improved the accuracy. The effectiveness of the proposed algorithm is verified by experimenting on UCI standard data sets and comparing with some multi-classification algorithms.