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Mass classification of benign and malignant with a new twin support vector machine joint \({l_{2,1}}\)-norm

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Abstract

Breast cancer is the second leading cause of cancer related death for women in the world, and mass is one of the most common kinds of abnormal. A mass can be either benign or malignant, the accurate diagnosis is important for early intervention and treatment. In this paper, we investigated the mass classification problem and proposed a new method for feature selection. The proposed method integrates joint \({l_{2,1}}\)-norm minimizing regularization with a nonparallel twin support vector machine, which is called TWSVML21. The \({l_{2,1}}\)-norm regularization selects features across positive and negative classes with joint sparsity, and features are selected by a ranking strategy. An iterative method is proposed to solve the involved optimization problem. Preliminary results on mass classification and several benchmark datasets showed the feasibility and effectiveness of the proposed TWSVML21 method.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (Nos. 61403287, 61472293, 31201121, 61572381, 61273303), China Postdoctoral Science Foundation (No. 2014M552039) and the Natural Science Foundation of Hubei Province (No. 2014CFB288).

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Authors and Affiliations

  1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China

    Xiaoming Liu, Ting Zhu, Leilei Zhai & Jun Liu

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065, China

    Xiaoming Liu, Ting Zhu, Leilei Zhai & Jun Liu

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  1. Xiaoming Liu
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Correspondence to Xiaoming Liu.

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Liu, X., Zhu, T., Zhai, L. et al. Mass classification of benign and malignant with a new twin support vector machine joint \({l_{2,1}}\)-norm. Int. J. Mach. Learn. & Cyber. 10, 155–171 (2019). https://doi.org/10.1007/s13042-017-0706-4

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