A hybrid PSO/ACO algorithm for discovering classification rules in data mining

Holden, Nicholas and Freitas, Alex A. (2008) A hybrid PSO/ACO algorithm for discovering classification rules in data mining. Journal of Artificial Evolution and Applications, 2008 . 11 pages. ISSN ISSN: 1687-6229. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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We have previously proposed a hybrid particle swarm optimisation/ant colony optimisation (PSO/ACO) algorithm for the discovery of classification rules. Unlike a conventional PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting nominal values into binary numbers in a preprocessing phase. PSO/ACO2 also directly deals with both continuous and nominal attribute values, a feature that current PSO and ACO rule induction algorithms lack. We evaluate the new version of the PSO/ACO algorithm (PSO/ACO2) in 27 public-domain, real-world data sets often used to benchmark the performance of classification algorithms. We compare the PSO/ACO2 algorithm to an industry standard algorithm PART and compare a reduced version of our PSO/ACO2 algorithm, coping only with continuous data, to our new classification algorithm for continuous data based on differential evolution. The results show that PSO/ACO2 is very competitive in terms of accuracy to PART and that PSO/ACO2 produces significantly simpler (smaller) rule sets, a desirable result in data mining—where the goal is to discover knowledge that is not only accurate but also comprehensible to the user. The results also show that the reduced PSO version for continuous attributes provides a slight increase in accuracy when compared to the differential evolution variant.

Item Type: Article
Uncontrolled keywords: particle swarm optimization, ant colony optimization, data mining, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 29 Mar 2010 12:09
Last Modified: 19 May 2014 15:57
Resource URI: https://kar.kent.ac.uk/id/eprint/23984 (The current URI for this page, for reference purposes)
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