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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Tackling Ant Colony Optimization Meta-Heuristic as Search Method in Feature Subset Selection Based on Correlation or Consistency Measures
verfasst von : Antonio J. Tallón-Ballesteros, José C. Riquelme
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2014
Verlag: Springer International Publishing
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This paper introduces the use of an ant colony optimization (ACO) algorithm, called Ant System, as a search method in two well-known 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.