Abstract
Feature selection is an important step in many pattern recognition systems that aims to overcome the so-called curse of dimensionality problem. Although Ant Colony Optimization (ACO) proved to be a powerful technique in different optimization problems, but it still needs some improvements when applied to the feature selection problem. This is due to the fact that it builds its solutions sequentially, where in feature selection this behavior will most likely not lead to the optimal solution. In this paper, a novel feature selection algorithm based on a combination of ACO and a simple, yet powerful, Differential Evolution (DE) operator is presented. The proposed combination enhances both the exploration and exploitation capabilities of the search procedure. The new algorithm is tested on two biosignal-driven applications. The performance of the proposed algorithm is compared with other dimensionality reduction techniques to prove its superiority.
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References
Liu, H., Motoda, H.: Computational Methods of Feature Selection. Taylor & Francis Group, Abington (2008)
Al-Ani, A.: Feature subset selection using ant colony optimization. Int. Journal of Computational Intelligence 2, 53–58 (2005)
Frohlich, H., Chapelle, O., Scholkopf, B.: Feature selection for support vector machines by means of genetic algorithms. In: 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2003), pp. 142–148 (2003)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, London (2004)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann, London (2001)
Firpi, H., Goodman, E.: Swarmed feature selection. In: Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop (AIPR 2004), pp. 112–118 (2004)
Price, K., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2005)
Zhang, C., Hu, H.: Feature selection using the hybrid of ant colony optimization and mutual information for the forecaster. In: Proceedings of International Conference on Machine Learning and Cybernetics, pp. 1728–1732 (2005)
Gao, H., Yang, H., Wang, X.: Ant colony optimization based network intrusion feature selection and detection. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, pp. 3871–3875 (2005)
Jensen, R.: Combining Rough and Fuzzy Sets for Feature Selection. PhD thesis, University of Edinburgh (2005)
Kanan, H., Faez, K., Taheri, S.: Feature selection using ant colony optimization (aco): A new method and comparative study in the application of face recognition system. In: Perner, P. (ed.) ICDM 2007. LNCS (LNAI), vol. 4597, pp. 63–76. Springer, Heidelberg (2007)
Yan, Z., Yuan, C.: Ant colony optimization for feature selection in face recognition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 221–226. Springer, Heidelberg (2004)
Al-Ani, A., Deriche, M., Chebil, J.: A new mutual information based measure for feature selection. Intelligent Data Analysis 7, 43–47 (2003)
Al-Ani, A., Al-Sukker, A.: Effect of feature and channel selection on eeg classification. In: Proceedings of The 28th IEEE EMBS Annual International Conference, New York City, USA, pp. 2171–2174 (2006)
Englehart, K.: Signal Representation for Classification of The Transient Myoelectric Signal. PhD thesis, University of New Brunswick (1998)
Chu, J., Moon, I., Mun, M.: A real-time emg pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand. IEEE Trans. on Biomedical Engineering 53(11), 2232–2239 (2006)
Chu, J., Moon, I., Mun, M.: A supervised feature projection for real-time multifunction myoelectric hand control. In: Proceedings of The 28th IEEE EMBS Annual International Conference, New York City, USA, pp. 2417–2420 (2006)
Chan, A., Green, G.: Myoelectric control development toolbox. In: Proceedings of The 30’th Conference of the Canadian Medical & Biological Engineering Society, Toronto, ON (2007)
Goge, A., Chan, A.: Investigating classification parameters for continuous myoelectrically controlled prostheses. In: Proceedings of The 28th Conference of the Canadian Medical & Biological Engineering Society, Quebec City, Canada, pp. 141–144 (2004)
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Khushaba, R.N., Al-Ani, A., AlSukker, A., Al-Jumaily, A. (2008). A Combined Ant Colony and Differential Evolution Feature Selection Algorithm. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_1
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DOI: https://doi.org/10.1007/978-3-540-87527-7_1
Publisher Name: Springer, Berlin, Heidelberg
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