Least squares support vector machines based on fuzzy rough set

Zhiwei Zhang, Degang Chen, Qiang He, Hui Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationUnknown Host Publication
Place of PublicationUnited States
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)978-1-4244-6586-6
Publication statusPublished - 22 Nov 2010

Bibliographical note

IEEE International Conference on Systems, Man and Cybernetics ; Conference date: 22-11-2010


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