υ-Nonparallel parametric margin fuzzy support vector machine

Abstract

Classification problem is an important research direction in machine learning. υ-nonparallel support vector machine (υ-NPSVM) is an important classifier used to solve classification problems. It is widely used because of its structural risk minimization principle, kernel trick, and sparsity. However, when solving classification problems, υ-NPSVM will encounter the problem of sample noises and heteroscedastic noise structure, which will affect its performance. In this paper, two improvements are made on the υ-NPSVM model, and a υ-nonparallel parametric margin fuzzy support vector machine (par-υ-FNPSVM) is established. On the one hand, for the noises that may exist in the data set, the neighbor information is used to add fuzzy membership to the samples, so that the contribution of each sample to the classification is treated differently. On the other hand, in order to reduce the effect of heteroscedastic structure, an insensitive loss function is introduced. The advantages of the new model are verified through UCI machine learning standard data set experiments. Finally, Friedman test and Bonferroni-Dunn test are used to verify the statistical significance of it.