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Ant Programming Algorithms for Classification
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Author(s): Juan Luis Olmo (University of Córdoba, Spain), José Raúl Romero (University of Córdoba, Spain)and Sebastián Ventura (University of Córdoba, Spain)
Copyright: 2014
Pages: 22
Source title:
Biologically-Inspired Techniques for Knowledge Discovery and Data Mining
Source Author(s)/Editor(s): Shafiq Alam (University of Auckland, New Zealand), Gillian Dobbie (University of Auckland, New Zealand), Yun Sing Koh (University of Auckland, New Zealand)and Saeed ur Rehman (Unitec Institute of Technology, New Zealand)
DOI: 10.4018/978-1-4666-6078-6.ch005
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Abstract
Ant programming is a kind of automatic programming that generates computer programs by using the ant colony metaheuristic as the search technique. It has demonstrated good generalization ability for the extraction of comprehensible classifiers. To date, three ant programming algorithms for classification rule mining have been proposed in the literature: two of them are devoted to regular classification, differing mainly in the optimization approach, single-objective or multi-objective, while the third one is focused on imbalanced domains. This chapter collects these algorithms, presenting different experimental studies that confirm the aptitude of this metaheuristic to address this data-mining task.
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