A Study of Different Quality Evaluation Functions in the cAnt-MinerPB Classification Algorithm

Medland, Matthew and Otero, Fernando E.B. (2012) A Study of Different Quality Evaluation Functions in the cAnt-MinerPB Classification Algorithm. In: Proceedings of the 2012 Genetic and Evolutionary Conference (GECCO 2012). (Full text available)

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Abstract

Ant colony optimization (ACO) algorithms for classification in general employ a sequential covering strategy to create a list of classification rules. A key component in this strategy is the selection of the rule quality function, since the algorithm aims at creating one rule at a time using an ACO-based procedure to search the best rule. Recently, an improved strategy has been proposed in the cAnt-MinerPB algorithm, where an ACO-based procedure is used to create a complete list of rules instead of individual rules. In the cAnt-MinerPB algorithm, the rule quality function has a smaller role and the search is guided by the quality of a list of rules. This paper sets out to determine the effect of different rule and list quality functions in terms of both predictive accuracy and size of the discovered model in cAnt-MinerPB. The comparative analysis is performed using 12 data sets from the UCI Machine Learning repository and shows that the effect of the rule quality functions in cAnt-MinerPB is different from the results previously presented in the literature.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: ant colony optimization, classification, sequential covering, rule quality functions, list quality functions
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Computational Intelligence Group
Depositing User: Fernando Otero
Date Deposited: 21 Sep 2012 09:49
Last Modified: 28 Nov 2014 21:41
Resource URI: https://kar.kent.ac.uk/id/eprint/30801 (The current URI for this page, for reference purposes)
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