Abstract
Feature Selection (FS) and reduction of pattern dimensionality is a most important step in pattern recognition systems. One approach in the feature selection area is employing population-based optimization algorithms such as Genetic Algorithm (GA)-based method and Ant Colony Optimization (ACO)-based method. This paper presents a novel feature selection method that is based on Ant Colony Optimization (ACO). ACO algorithm is inspired of ant’s social behavior in their search for the shortest paths to food sources. Most common techniques for ACO-Based feature selection use the priori information of features. However, in the proposed algorithm, classifier performance and the length of selected feature vector are adopted as heuristic information for ACO. So, we can select the optimal feature subset without the priori information of features. This approach is easily implemented and because of using one simple classifier in it, its computational complexity is very low. Simulation results on face recognition system and ORL database show the superiority of the proposed algorithm.
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Kanan, H.R., Faez, K., Taheri, S.M. (2007). Feature Selection Using Ant Colony Optimization (ACO): A New Method and Comparative Study in the Application of Face Recognition System. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_6
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DOI: https://doi.org/10.1007/978-3-540-73435-2_6
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