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

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摘要

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.

论文关键词:Data mining,Multi-objective optimization,Particle swarm optimization,Chaotic maps

论文评审过程:Received 22 February 2008, Accepted 4 June 2009, Available online 10 June 2009.

论文官网地址:https://doi.org/10.1016/j.knosys.2009.06.004