All-in-one multicategory Ramp loss maximum margin of twin spheres support vector machine
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Maximum margin of twin spheres support vector machine (MMTSSVM) is effective to deal with imbalanced data classification problems. However, it is sensitive to outliers because of the use of the Hinge loss function. To enhance the stability of MMTSSVM, we propose a Ramp loss maximum margin of twin spheres support vector machine (Ramp-MMTSSVM) in this paper. In terms of the Ramp loss function, the outliers can be given fixed loss values, which reduces the negative effect of outliers on constructing models. Since Ramp-MMTSSVM is a non-differentiable non-convex optimization problem, we adopt Concave-Convex Procedure (CCCP) approach to solve it. We also analyze the properties of parameters and verify them by one artificial experiment. Besides, we use Rest-vs.-One(RVO) strategy to extend Ramp-MMTSSVM to multi-class classification problems. The experimental results on twenty benchmark datasets indicate that no matter in binary or multi-class classification cases, our approaches both can obtain better experimental performance than the compared algorithms.
KeywordsMMTSSVM Ramp loss CCCP Multi-class
This work was supported in part by National Natural Science Foundation of China (No.11671010).
- 5.Ghosh S, Mondal S, Ghosh B (2014) A comparative study of breast cancer detection based on SVM and MLP BPN classifier. In: First international conference on automation, control, energy & systems (ACES-14), pp 87–90Google Scholar
- 6.Gohariyan E, Esmaeilpour M, Shirmohammadi MM (2017) The combination of mammography and MRI for diagnosing breast cancer using fuzzy NN and SVM. Int J Interact Multimed Artif Intell 4(5):20–24Google Scholar
- 7.Naz S, Ziauddin S, Shahid AR (2018) Driver fatigue detection using mean intensity, SVM, and SIFT. Int J Interact Multimed Artif Intell 5(IP):1Google Scholar