Abstract
In this paper we propose a new model of ACO called Two-Step AntColony System. The basic idea is to split the heuristic search performed by ants into two stages. We have studied the performance of this new algorithm for the Feature Selection Problem. Experimental results obtained show the Two-Step approach significantly improves the Ant Colony System in term of computation time needed.
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Bello, P.R., Nowe, A., et al.: Using ACO and Rough Set Theory to Feature Selection. WSEAS Transactions on Information Science and Applications 2(5), 512–517 (2005)
Bello, P.R., Nowe, A., et al.: A Model based on Ant Colony System and Rough Set Theory to Feature Selection. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2005), Washington DC, USA, June 25-29, pp. 275–276. ACM Press, New York (2005)
Bello, P.R., Nowe, A., et al.: Using Ant Colony System meta-heuristic and Rough Set Theory to Feature Selection. In: Proceedings of The 6th Metaheuristics International Conference (MIC2005), Vienna, Austria, August 22-26, University of Vienna (2005)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Dong, M., Kothari, R.: Feature subset selection using a new definition of classifiability. Patter Recognition Letter 24(9-10), 1215–1225 (2003)
Dorigo, M., et al.: The Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics-Part B 26(1), 1–13 (1996)
Dorigo, M., Gambardela, L.M.: Ant colonies for the traveling salesman problem. BioSystems (43), 73–81 (1997)
Dorigo, M., et al.: Ant algorithms for Discrete optimization. Artificial Life 5(2), 137–172 (1999)
Dorigo, M., Stützle, T.: ACO Algorithms for the Traveling Salesman Problem, Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications, John Wiley & Sons., EEUU (1999)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Inza, I., et al.: Feature subset selection by bayesian networks based optimization. Artificial intelligence 123(1-2), 157–184 (2000)
Jensen, R., Shen., Q.: Finding rough set reducts with ant colony optimization. In: Proceedings of UK Workshop on Computational Intelligence, pp. 15–22 (2003)
Komorowski, J., Pawlak, Z., et al.: Rough Sets: A tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization: A new trend in decision-making, pp. 3–98. Springer, Heidelberg (1999)
Kudo, M., Sklansky, J.: Comparison of algorithms that select features for pattern classifiers. Pattern Recognition 33, 25–41 (2000)
Somol, P., Pudil, P.: Feature selection toolbox. Pattern Recognition 35, 2749–2759 (2002)
Stefanowski, J.: An experimental evaluation of improving rule based classifiers with two approaches that change representations of learning examples. Engineering Applications of Artificial Intelligence 17, 439–445 (2004)
Xing, H., Xu, L.: Feature space theory –a mathematical foundation for data mining. Knowledge-based systems 14, 253–257 (2001)
Zhang, H., Sun, G.: Feature selection using tabu search method. Pattern Recognition 35, 710–711 (2002)
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Bello, R., Puris, A., Nowe, A., Martínez, Y., García, M.M. (2006). Two Step Ant Colony System to Solve the Feature Selection Problem. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2006. Lecture Notes in Computer Science, vol 4225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892755_61
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DOI: https://doi.org/10.1007/11892755_61
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