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Tackling Ant Colony Optimization Meta-Heuristic as Search Method in Feature Subset Selection Based on Correlation or Consistency Measures

 

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Opened Access Tackling Ant Colony Optimization Meta-Heuristic as Search Method in Feature Subset Selection Based on Correlation or Consistency Measures
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Author: Tallón Ballesteros, Antonio Javier
Riquelme Santos, José Cristóbal
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2014
Published in: Intelligent Data Engineering and Automated Learning – IDEAL 2014: 15th International Conference, Salamanca, Spain, September 10-12, 2014. Proceedings. Lecture Notes in Computer Science, v.8669
ISBN/ISSN: 978-3-319-10839-1
0302-9743
Document type: Chapter of Book
Abstract: This paper introduces the use of an ant colony optimization (ACO) algorithm, called Ant System, as a search method in two wellknown feature subset selection methods based on correlation or consistency measures such as CFS (Correlation-based Feature Selection) and CNS (Consistency-based Feature Selection). ACO guides the search using a heuristic evaluator. Empirical results on twelve real-world classification problems are reported. Statistical tests have revealed that InfoGain is a very suitable heuristic for CFS or CNS feature subset selection methods with ACO acting as search method. The use of InfoGain is shown to be the significantly better heuristic over a range of classifiers. The results achieved by means of ACO-based feature subset selection with the suitable heuristic evaluator are better for most of the problems comparing with those obtained with CFS or CNS combined with Best First search.
Size: 188.4Kb
Format: PDF

URI: http://hdl.handle.net/11441/42721

DOI: http://dx.doi.org/10.1007/978-3-319-10840-7_47

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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