Abstract
In practice, classifiers are often build based on data or heuristic information. The number of potential features is usually large. One of the most important tasks in classification systems is to identify the most relevant features, because less relevant features can be interpreted as noise that reduces the classification accuracy, even for fuzzy classifiers which are somehow robust to noise. This paper proposes an ant colony optimization (ACO) algorithm for the feature selection problem. The goal is to find the set of features that reveals the best classification accuracy for a fuzzy classifier. The performance of the method is compared to other features selection methods based on tree search methods.
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Vieira, S.M., Sousa, J.M.C., Runkler, T.A. (2007). Ant Colony Optimization Applied to Feature Selection in Fuzzy Classifiers. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds) Foundations of Fuzzy Logic and Soft Computing. IFSA 2007. Lecture Notes in Computer Science(), vol 4529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72950-1_76
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DOI: https://doi.org/10.1007/978-3-540-72950-1_76
Publisher Name: Springer, Berlin, Heidelberg
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