Affiliations: [a]
College of Science, China Agriculture University, Beijing, China
| [b]
School of Information, Beijing WUZI University, Beijing, China
| [c]
School of Management, Qufu Normal University, Rizhao, Shandong, China
Correspondence:
[*]
Correspondence to: Ling Jing, College of Science, China Agriculture University, Beijing, China. E-mail: jingling@cau.edu.cn.
Abstract: By using fuzzy set theory (FS), interval-valued fuzzy set theory (IVFS) and nonparallel support vector machine theory (NPSVM), the fuzzy nonparallel support vector machine (F-NPSVM) and interval-valued fuzzy nonparallel support vector machine (IVF-NPSVM) are constructed. Both F-NPSVM and IVF-NPSVM consider the membership degree of the training points in their models and the difference is the method to determine them. Then the solutions to them are derived. The experiments on both artificial data set and benchmark data sets show that most of the classification results by using the F-NPSVM and IVF-NPSVM are more accurate than NPSVM, support vector machine (SVM), interval-valued fuzzy support vector machine (IVF-SVM), generalized eigenvalue proximal support vector machine (GEPSVM) and twin support vector machine (TWSVM). Finally, Friedman test is used to verify that there is a significant difference between the two new models and the previous ones.
Keywords: Classification, fuzzy, interval-valued fuzzy, nonparallel support vector machine
