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Multi-objective rule mining using a chaotic particle swarm optimization algorithm
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DOI：10.1016/j.knosys.2009.06.004.png)
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Abstract
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In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm. (C) 2009 Elsevier B.V. All rights reserved.
Keywords:
Data mining
Multi-objective optimization
Particle swarm optimization
Chaotic maps
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