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Least squares support vector machines based on fuzzy rough set

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4 Author(s)
Zhi-Wei Zhang ; Dept. of Math. & Phys., North China Electr. Power Univ., Beijing, China ; De-Gang Chen ; Qiang He ; Hui Wang

In this paper, a new approach to improve least squares support vector machines is presented. We consider the membership of every sample in constraints, that is to say, every sample are not fully assigned to one class. The membership is computed by employing the technique of fuzzy rough sets, and then a new least squares support vector machine algorithm based on fuzzy rough sets is proposed, experiments are carried out to show that our idea in this paper is feasible and valid.

Published in:

Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on

Date of Conference:

10-13 Oct. 2010

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