Least squares support vector machines based on fuzzy rough set

Zhiwei Zhang; Degang Chen; Qiang He; Hui Wang

doi:10.1109/ICSMC.2010.5642029

Least squares support vector machines based on fuzzy rough set

Zhiwei Zhang, Degang Chen, Qiang He, Hui Wang

School of Electronics, Electrical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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 language	English
Title of host publication	Unknown Host Publication
Place of Publication	United States
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	978-1-4244-6586-6
DOIs	https://doi.org/10.1109/ICSMC.2010.5642029
Publication status	Published - 22 Nov 2010

Bibliographical note

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

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10.1109/ICSMC.2010.5642029

Cite this

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title = "Least squares support vector machines based on fuzzy rough set",

abstract = "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.",

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AU - Chen, Degang

AU - He, Qiang

AU - Wang, Hui

N1 - IEEE International Conference on Systems, Man and Cybernetics ; Conference date: 22-11-2010

PY - 2010/11/22

Y1 - 2010/11/22

N2 - 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.

AB - 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.

U2 - 10.1109/ICSMC.2010.5642029

DO - 10.1109/ICSMC.2010.5642029

M3 - Conference contribution

SN - 978-1-4244-6586-6

BT - Unknown Host Publication

PB - Institute of Electrical and Electronics Engineers Inc.

CY - United States

ER -

Least squares support vector machines based on fuzzy rough set

Abstract

Bibliographical note

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