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A New Approach to Associative Classification Based on Binary Multi-objective Particle Swarm Optimization

A New Approach to Associative Classification Based on Binary Multi-objective Particle Swarm Optimization

Madhabananda Das, Rahul Roy, Satchidananda Dehuri, Sung-Bae Cho
Copyright: © 2011 |Volume: 2 |Issue: 2 |Pages: 23
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781613505687|DOI: 10.4018/jamc.2011040103
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MLA

Das, Madhabananda, et al. "A New Approach to Associative Classification Based on Binary Multi-objective Particle Swarm Optimization." IJAMC vol.2, no.2 2011: pp.51-73. http://doi.org/10.4018/jamc.2011040103

APA

Das, M., Roy, R., Dehuri, S., & Cho, S. (2011). A New Approach to Associative Classification Based on Binary Multi-objective Particle Swarm Optimization. International Journal of Applied Metaheuristic Computing (IJAMC), 2(2), 51-73. http://doi.org/10.4018/jamc.2011040103

Chicago

Das, Madhabananda, et al. "A New Approach to Associative Classification Based on Binary Multi-objective Particle Swarm Optimization," International Journal of Applied Metaheuristic Computing (IJAMC) 2, no.2: 51-73. http://doi.org/10.4018/jamc.2011040103

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Abstract

Associative classification rule mining (ACRM) methods operate by association rule mining (ARM) to obtain classification rules from a previously classified data. In ACRM, classifiers are designed through two phases: rule extraction and rule selection. In this paper, the ACRM problem is treated as a multi-objective problem rather than a single objective one. As the problem is a discrete combinatorial optimization problem, it was necessary to develop a binary multi-objective particle swarm optimization (BMOPSO) to optimize the measure like coverage and confidence of association rule mining (ARM) to extract classification rules in rule extraction phase. In rule selection phase, a small number of rules are targeted from the extracted rules by BMOPSO to design an accurate and compact classifier which can maximize the accuracy of the rule sets and minimize their complexity simultaneously. Experiments are conducted on some of the University of California, Irvine (UCI) repository datasets. The comparative result of the proposed method with other standard classifiers confirms that the new proposed approach can be a suitable method for classification.

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