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.
|Title of host publication||Unknown Host Publication|
|Place of Publication||United States|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 22 Nov 2010|