A Method for Reducing the Number of Support Vectors in Fuzzy Support Vector Machine

Nguyễn Mạnh Cường, Nguyễn Văn Thiện

A METHOD FOR REDUCING THE NUMBER OF SUPPORT VECTORS IN FUZZY SUPPORT VECTOR MACHINE

Nguyễn Mạnh Cường, Nguyễn Văn Thiện

Springer, Cham

2016

Abstract

We offer an efficient method to reduce the number of support vectors for Fuzzy Support Vector Machine. Firstly, we consider the Fuzzy Support Vector Machine model which was proposed by Lin and Wang. For the reducing the number of support vectors, we apply the l0l0 regularization term to the dual form of this model. The resulting optimization problem is non-smooth and non-convex. The l0l0 is then replaced by an approximation function. An algorithm which is based on DC programming and DCA is then investigated to solve this problem. Numerical results on real-world datasets show the efficiency and the superiority of our method versus the standard algorithm on both support vector reduction and classification.

Citation

Manh Cuong N., Van Thien N. (2016) A Method for Reducing the Number of Support Vectors in Fuzzy Support Vector Machine. In: Nguyen T., van Do T., An Le Thi H., Nguyen N. (eds) Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing, vol 453. Springer, Cham

Full paper pdf: A METHOD FOR REDUCING THE NUMBER OF SUPPORT VECTORS IN FUZZY SUPPORT VECTOR MACHINE

A Method for Reducing the Number of Support Vectors in Fuzzy Support Vector Machine