A bilateral-truncated-loss based robust support vector machine for classification problems

Support vector machine (SVM) is sensitive to outliers or noise in the training dataset. Fuzzy SVM (FSVM) and the bilateral-weighted FSVM (BW-FSVM) can partly overcome this shortcoming by assigning different fuzzy membership degrees to different training samples. However, it is a difficult task to se...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2015-10, Vol.19 (10), p.2871-2882
Main Authors: Yang, Xiaowei, Han, Le, Li, Yan, He, Lifang
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Classification
Computational Intelligence
Control
Datasets
Distance learning
Engineering
Fuzzy sets
Iterative algorithms
Iterative methods
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Noise sensitivity
Optimization
Outliers (statistics)
Robotics
Robustness
Statistical tests
Support vector machines
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Summary:Support vector machine (SVM) is sensitive to outliers or noise in the training dataset. Fuzzy SVM (FSVM) and the bilateral-weighted FSVM (BW-FSVM) can partly overcome this shortcoming by assigning different fuzzy membership degrees to different training samples. However, it is a difficult task to set the fuzzy membership degrees of the training samples. To avoid setting fuzzy membership degrees, from the beginning of the BW-FSVM model, this paper outlines the construction of a bilateral-truncated-loss based robust SVM (BTL-RSVM) model for classification problems with noise. Based on its equivalent model, we theoretically analyze the reason why the robustness of BTL-RSVM is higher than that of SVM and BW-FSVM. To solve the proposed BTL-RSVM model, we propose an iterative algorithm based on the concave–convex procedure and the Newton–Armijo algorithm. A set of experiments is conducted on ten real world benchmark datasets to test the robustness of BTL-RSVM. The statistical tests of the experimental results indicate that compared with SVM, FSVM and BW-FSVM, the proposed BTL-RSVM can significantly reduce the effects of noise and provide superior robustness.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-014-1448-9