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Mathematical Problems in Engineering - Volume 2014 2014, Article ID 539430, 6 pages -

Research Article

School of Mathematics and Information Science, North National University, Yinchuan 750021, China

Business School, North National University, Yinchuan 750021, China

Received 20 October 2013; Revised 27 January 2014; Accepted 9 February 2014; Published 16 March 2014

Academic Editor: Cheng Shao

Copyright © 2014 Chuandong Qin and Huixia Zhao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Imbalanced data learning is one of the most active and important fields in machine learning research. The existing class imbalance learning methods can make Support Vector Machines SVMs less sensitive to class imbalance; they still suffer from the disturbance of outliers and noise present in the datasets. A kind of Fuzzy Smooth Support Vector Machines FSSVMs are proposed based on the Smooth Support Vector Machine SSVM of O. L. Mangasarian. SSVM can be computed by the Broyden-Fletcher-Goldfarb-Shanno BFGS algorithm or the Newton-Armijo algorithm easily. Two kinds of fuzzy memberships and three smooth functions can be chosen in the algorithms. The fuzzy memberships consider the contribution rate of each sample to the optimal separating hyperplane. The polynomial smooth functions can make the optimization problem more accurate at the inflection point. Those changes play the active effects on trials. The results of the experiments show that the FSSVMs can gain the better accuracy and the shorter time than the SSVMs and some of the other methods.





Autor: Chuandong Qin and Huixia Zhao

Fuente: https://www.hindawi.com/



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