Automatic parameter settings for the PROAFTN classifier using Hybrid Particle Swarm Optimization

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Proceedings titleProceedings of the 23rd Canadian Conference on Artificial Intelligence (AI 2010)
Conference23rd Canadian Conference on Artificial Intelligence (AI 2010), May 31, 2010 - Jun 2, 2010, Ottawa, Ontario
Pages184195; # of pages: 12
SubjectKnowledge Discovery; Particle swarm optimization; Reduced Variable Neighborhood Search; Multiple criteria classification; PROAFTN; Supervised Learning
AbstractIn this paper, a new hybrid metaheuristic learning algorithm is introduced to choose the best parameters for the multi-criteria decision making algorithm (MCDA) called PROAFTN. PROAFTN requires values of several parameters to be determined prior to classification. These parameters include boundaries of intervals and relative weights for each attribute. The proposed learning algorithm, identified as PSOPRO-RVNS as it integrates particle swarm optimization (PSO) and Reduced Variable Neighborhood Search (RVNS), is used to automatically determine all PROAFTN parameters. The combination of PSO with RVNS allows to improve the exploration and exploitation capabilities of PSO by setting some search points to be iteratively re-exploited using RVNS. Based on the generated results, experimental evaluations show that PSOPRO-RVNS outperforms six well-known machine learning classifiers in a variety of problems.
Publication date
PublisherNational Research Council of Canada
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Access conditionavailable
Peer reviewedYes
NPARC number15336800
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Record identifierbdf5b4f8-cc28-448e-90b8-af444df5189f
Record created2010-06-10
Record modified2016-05-09
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