Ramp loss K-Support Vector Classification-Regression; a robust and sparse multi-class approach to the intrusion detection problem

作者：

Highlights：

• A robust and sparse multi-class approach for Multi-Class classification is proposed.

• The proposed method is based on Ramp loss K-Support Vector Classification-Regression.

• The CCCP procedure is used to solve a non-differentiable non-convex optimization problem.

• ADMM is adopted to make our model well-adapted for the large-scale setting.

• The results of Ramp-KSVCR show superior generalization power and low computational cost.

摘要

•A robust and sparse multi-class approach for Multi-Class classification is proposed.•The proposed method is based on Ramp loss K-Support Vector Classification-Regression.•The CCCP procedure is used to solve a non-differentiable non-convex optimization problem.•ADMM is adopted to make our model well-adapted for the large-scale setting.•The results of Ramp-KSVCR show superior generalization power and low computational cost.

论文关键词：Multi-Class classification,Intrusion detection,K-Support Vector Classification-Regression,Ramp loss function,Alternating Direction Method of Multipliers (ADMM),Concave–Convex Procedure (CCCP)

论文评审过程：Received 21 July 2016, Revised 14 March 2017, Accepted 15 March 2017, Available online 16 March 2017, Version of Record 2 May 2017.

论文官网地址：https://doi.org/10.1016/j.knosys.2017.03.012