Automatic parameter settings for the PROAFTN classifier using hybrid particle swarm optimization

Authors:
Feras Al-Obeidat;Nabil Belacel;Juan A. Carretero;Prabhat Mahanti
Affiliations:
National Research Council Canada, University of New Brunswick, Canada;National Research Council Canada, University of New Brunswick, Canada;National Research Council Canada, University of New Brunswick, Canada;National Research Council Canada, University of New Brunswick, Canada
Venue:
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Year:
2010

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Abstract

In this paper, a new hybrid metaheuristic learning algorithm is introduced to choose the best parameters for the classification method PROAFTN PROAFTN is a multi-criteria decision analysis (MCDA) method which 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 capabilities of PSO by setting some search points to be iteratively re-explored 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.