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Robust Twin Bounded Support Vector Machines for Outliers and Imbalanced Data by Parashjyoti Borah; Deepak Gupta is a Computer Science article available to read on EtoBox.

What is Robust Twin Bounded Support Vector Machines for Outliers and Imbalanced Data about?

Truncated loss functions are robust to class noise and outliers. A robust twin bounded support vector machine is proposed in this paper that truncates the growth of its loss functions at a pre-specified point, thus, flattens the function that pre-specified score afterwards. Moreover, to make the proposed method capable of handling datasets with different imbalance ratio, cost-sensitive learning is implemented by scaling the total error of the classes based on the number of samples of each class. However, the adopted loss functions take a non-convex structure which does not always assure global optimum. To handle this issue, we suggest concave-convex procedure (CCCP) to ensure global convergence by decomposing the cost functions into additions of one convex and one concave part. The behaviour of the proposed method for varied imbalance ratio is analysed experimentally and depicted graphically. Classification performance of the proposed method in terms of AUC, F-Measure and G-Mean is compared with other related methods, viz. Hinge loss support vector machine (SVM), Ramp loss SVM (RSVM), twin SVM (TWSVM), twin bounded SVM (TBSVM), Pinball loss SVM (pin-SVM), entropy-based fuzzy SVM (E

Who reads Robust Twin Bounded Support Vector Machines for Outliers and Imbalanced Data?

Author

Parashjyoti Borah; Deepak Gupta

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)