MULTI - OBJECTIVE PARTICLE SWARM CLASSIFIER

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ZAHIRI S.H.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, A- MUHANDISI -I BARQ Year:2006 | Volume:4 | Issue:2 Page(s): 91-98

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Information Journal Paper

Title

MULTI - OBJECTIVE PARTICLE SWARM CLASSIFIER

Author(s)

ZAHIRI S.H. | Issue Writer Certificate

Keywords

MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATIONQ4

PATTERN RECOGNITIONQ3

CLASSIFIERQ3

SWARM INTELLIGENCEQ2

Abstract

A MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION (MOPSO) algorithm has been used to design a CLASSIFIER which is able to optimize some important PATTERN RECOGNITION indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The proposed CLASSIFIER can efficiently approximate the decision hyperplanes for separating the different classes in the feature space and dose not has any over-fitting and over-learning problems.Other SWARM INTELLIGENCE based CLASSIFIERs do not have the capability of simultaneous optimizing aforesaid indices and they also may suffer the over-fitting problem.The experimental results show that the proposed multi-objective CLASSIFIER can estimate the optimum sets of hyperplanes by approximating the Pareto-front and provide the favorite user's setup for selecting aforesaid indices.

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APA: Copy

ZAHIRI, S.H.. (2006). MULTI - OBJECTIVE PARTICLE SWARM CLASSIFIER. NASHRIYYAH-I MUHANDESI-I BARQ VA MUHANDESI-I KAMPYUTAR-I IRAN (PERSIAN), 4(2), 91-98. SID. https://sid.ir/paper/53701/en

Vancouver: Copy

ZAHIRI S.H.. MULTI - OBJECTIVE PARTICLE SWARM CLASSIFIER. NASHRIYYAH-I MUHANDESI-I BARQ VA MUHANDESI-I KAMPYUTAR-I IRAN (PERSIAN)[Internet]. 2006;4(2):91-98. Available from: https://sid.ir/paper/53701/en

IEEE: Copy

S.H. ZAHIRI, “MULTI - OBJECTIVE PARTICLE SWARM CLASSIFIER,” NASHRIYYAH-I MUHANDESI-I BARQ VA MUHANDESI-I KAMPYUTAR-I IRAN (PERSIAN), vol. 4, no. 2, pp. 91–98, 2006, [Online]. Available: https://sid.ir/paper/53701/en

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