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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References
kml, L., Kittler, J.: Feature set search algorithms. In: Chen, C.H. (ed.) Pattern Recognition and Signal Processing, Sijhoff and Noordhoff, the Netherlands (1978)
Ani, A.A.: An Ant Colony Optimization Based Approach for Feature Selection. In: Proceeding of AIML Conference (2005)
Jensen, R.: Combining rough and fuzzy sets for feature selection. Ph.D. Thesis, University of Edinburgh (2005)
Kohavi, R.: Feature Subset Selection as search with Probabilistic Estimates. AAAI Fall Symposium On Relevance (1994)
Srinivas, M., Patnik, L.M.: Genetic Algorithms: A Survey. IEEE Computer Society Press, Los Alamitos (1994)
Dorigo, M., Caro, G.D.: Ant Colony Optimization: A New Meta-heuristic. In: Proceeding of the Congress on Evolutionary Computing (1999)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
Liu, B., Abbass, H.A., McKay, B.: Classification Rule Discovery with Ant Colony Optimization. IEEE Computational Intelligence 3(1) (2004)
Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B 26(1), 29–41 (1996)
Maniezzo, V., Colorni, A.: The Ant System Applied to the Quadratic Assignment Problem. Knowledge and Data Engineering 11(5), 769–778 (1999)
Duda, R.O., Hart, P.E.: Pattern Recognition and Scene Analysis. Wiley, Chichester (1973)
Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15, 1119–1125 (1994)
Siedlecki, W., Sklansky, J.: A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters 10(5), 335–347 (1989)
Ani, A.A.: Ant Colony Optimization for Feature Subset Selection. Transactions On Engineering, Computing And Technology 4 (2005)
Zhang, C.K., Hu, H.: Feature Selection Using The Hybrid Of Ant Colony Optimization and Mutual Information For The Forecaster. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics (2005)
Gao, H.H., Yang, H.H., Wang, X.Y.: Ant Colony Optimization Based Network Intrusion Feature Selection And Detection. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics (2005)
Bins, J.: Feature Selection of Huge Feature Sets in the Context of Computer Vision. Ph.D. Dissertation, Computer Science Department, Colorado State University (2000)
Siedlecki, W., Sklansky, J.: On Automatic Feature Selection. International Journal of Pattern Recognition and Artificial Intelligence 2(2), 197–220 (1988)
Dorigo, M., Blum, C.: Ant colony optimization theory: A survey. Theoretical Computer Science 344, 243–278 (2005)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991)
Yang, J., Honavar, V.: Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems 13, 44–49 (1998)
Punch, W.F., Goodman, E.D., Pei, L.C.S.M., Hovland, P., Enbody, R.: Further research on Feature Selection and Classification using Genetic Algorithms. In: Proc. Int. Conf. Genetic Algorithms, pp. 557–564 (1993)
Raymer, M., Punch, W., Goodman, E., Kuhn, L., Jain, A.K.: Dimensionality Reduction Using Genetic Algorithms. IEEE Transactions on Evolutionary Computing 4, 164–171 (2000)
Rashidy Kanan, H., Faez, K., Ezoji, M.: Face Recognition: An Optimized Localization Approach and Selected PZMI Feature Vector Using SVM Classifier. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS, vol. 4113, pp. 690–696. Springer, Heidelberg (2006)
Rashidy Kanan, H., Faez, K., Ezoji, M.: An Efficient Face Recognition System Using a New Optimized Localization Method. In: ICPR 2006. Proceeding of the 18th International Conference on Pattern Recognition (2006)
Rashidy Kanan, H., Faez, K.: ZMI and Wavelet Transform Features and SVM Classifier in the Optimized Face Recognition system. In: ISSPIT 2005. Proceeding of the 5th IEEE International Symposium on Signal Processing and Information Technology, pp. 295–300. IEEE Computer Society Press, Los Alamitos (2005)
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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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