Multi-objective rule mining using a chaotic particle swarm optimization algorithm

Abstract

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. 
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