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Discovering Regression Rules with Ant Colony Optimization

Brookhouse, James, Otero, Fernando E.B. (2015) Discovering Regression Rules with Ant Colony Optimization. In: Proceedings of the 2015 Genetic and Evolutionary Conference Companion (GECCO'15 Companion). . pp. 1005-1012. ACM Press (doi:10.1145/2739482.2768450)

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http://dx.doi.org/10.1145/2739482.2768450

Abstract

The majority of Ant Colony Optimization (ACO) algorithms for data mining have dealt with classification or clustering problems. Regression remains an unexplored research area to the best of our knowledge. This paper proposes a new ACO algorithm that generates regression rules for data mining applications. The new algorithm combines components from an existing deterministic (greedy) separate and conquer algorithm—employing the same quality metrics and continuous attribute processing techniques—allowing a comparison of the two. The new algorithm has been shown to decrease the relative root mean square error when compared to the greedy algorithm. Additionally a different approach to handling continuous attributes was investigated showing further improvements were possible.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1145/2739482.2768450
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 29 May 2015 12:52 UTC
Last Modified: 01 Aug 2019 10:38 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48689 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
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