Introducing a New Version of Binary Ant Colony Algorithm to Solve the Problem of Feature Selection
Subject Areas : electrical and computer engineeringS. Kashef 1 , H. Nezamabadi-pour 2
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Keywords: Feature selection wrapper Ant colony optimization (ACO) binary ACO classification,
Abstract :
The use of metaheuristic algorithms is a good choice for solving optimization problems. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. This algorithm is an advanced version of binary ant colony optimization, which attempts to solve the problems of ACO and BACO algorithms by combination of these two. The performance of proposed algorithm is compared to the performance of Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), and some prominent ACO-based algorithms on the task of feature selection on 12 well-known UCI datasets. Simulation results verify that the algorithm provides a suitable feature subset with good classification accuracy using a smaller feature set than competing feature selection methods.
[1] M. Kabir, Md. Shahjahan, and K. Murase, "A new local search standard, based hybrid genetic algorithm for feature selection," Neurocomputing, vol. 74, no. 17, pp. 2914-2928, Oct. 2011.
[2] S. M. Vieira, J. M. C. Sousa, and T. A. Runkler, "Two cooperative ant colonies for feature selection using fuzzy models," Expert Systems with Applications, vol. 37, no. 4, pp. 2714-2723, Apr. 2010.
[3] I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. of Machine Learning Research, vol. 3, pp. 1157-1182, Mar. 2003.
[4] M. Dash and H. Liu, "Feature selection for classification," Intelligent Data Analysis, vol. 1, no. 1-4, pp. 131-156, 1997.
[5] H. Liu and L. Yu, "Toward integrating feature selection algorithms for classification and clustering," IEEE Trans. on Knowledge and Data Engineering, vol. 17, no. 4, pp. 491-502, Apr. 2005.
[6] S. Ding, "Feature selection based F- score and ACO algorithm in support vector machine," in Proc. 2nd Int. Symp. on Knowledge Acquisition and Modeling, vol. 1, pp. 10-23, Nov. 2009.
[7] L. T. Vinh, S. Lee, Y. T. Park, and B. J. d'Auriol, "A novel feature selection method based on normalized mutual information," Appl Intell, vol. 37, no. 1, pp. 100-120, Jul. 2010.
[8] M. H. Aghdam, N. Ghasem-Aghaee, and M. E. Basiri, "Text feature selection using ant colony optimization," Expert Systems with Applications, vol. 36, no. 3, pp. 6843-6853, Apr. 2009.
[9] M. Kabir, M. Islam, and K. Murase, "A new wrapper feature selection approach using neural network," Neurocomputing, vol. 73, no. 16-18, pp. 3273-3283, Oct. 2011.
[10] R. Kohavi and G. John, "Wrappers for feature selection," Artificial Intelligence, vol. 97, no. 1-2, pp. 273-324, Dec. 1997.
[11] P. Bermejo, J. A. Gamez, and J. M. Puerta, "A GRASP algorithm for fast hybrid (filter - wrapper) feature subset selection in high - dimensional datasets," Pattern Recognition Letters, vol. 32, no. 5, pp. 701-711, Apr. 2011.
[12] J. Huang, Y. Cai, and X. Xu, "A hybrid genetic algorithm for feature selection wrapper based on mutual information," Pattern Recognition Letters, vol. 28, no. 13, pp. 1825-1844, Oct. 2007.
[13] R. K. Sivagaminathan and S. Ramakrishnan, "A hybrid approach for feature subset selection using neural networks and ant colony optimization," Expert Systems with Applications, vol. 33, no. 1, pp. 49-60, Jul. 2007.
[14] I. Oh, J. S. Lee, and B. R. Moon, "Hybrid genetic algorithm for feature selection," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1424-1437, Nov. 2004.
[15] س. م. رضوي، ﻫ. صدوقي يزدي و ا. کبير، "انتخاب ويژگي براي بازشناسي ارقام دست‌نويس فارسي به کمک الگوريتم وراثتي،" هفتمين کنفرانس سالانه انجمن کامپيوتر ايران، صص. 285-292، 1380.
[16] م. رستمي شهر بابکي و ح. نظام آبادي پور، "انتخاب ويژگي در طبقه‌بندي معنايي تصاوير با استفاده از الگوريتم PSO،" پانزدهمين کنفرانس سالانه مهندسي کامپيوتر ايران، صص. 269-275، 1384.
[17] L. Y. Chuang, S. W. Tsai, and C. H. Yang, "Improved binary particle swarm optimization using catfish effect for feature selection," Expert Systems with Applications, vol. 38, no. 10, pp. 12699-12707, Sep. 2011.
[18] E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, "A simultaneous feature adaptation and feature selection method for content - based image retrieval systems," Knowledge Based System, vol. 39, pp. 85-94, Feb. 2013.
[19] E. Rashedi and H. Nezamabadi-pour, "Feature subset selection using improved binary gravitational search algorithm," J. of Intelligent and Fuzzy Systems, vol. 26, no. 3, pp. 1211-1221, May 2013.
[20] ع. راشدي، ح. نظام آبادي پور و ح. توحيدي، "انتخاب ويژگي با استفاده از الگوريتم جستجوي گرانشي"، سومين کنفرانس بين‌المللي فناوري اطلاعات و دانش، مشهد، آذر 1386.
[21] S. Sarafrazi and H. Nezamabadi-pour, "Facing the classification of binary problems with a GSA-SVM hybrid system," Mathematical and Computer Modelling, vol. 57, no. 1-2, pp. 270-278, Jan. 2013.
[22] M. Dorigo and L. M. Gambardella, "Ant colonies for the traveling salesman problem," BioSystems, vol. 43, no. 2, pp. 73-81, Jul. 1997.
[23] M. Dorigo and L. M. Gambardella, "Ant colony system: a cooperative learning approach to the traveling salesman problem," IEEE Trans. on Evolutionary Computation, vol. 1, no. 1, pp. 53-66, Apr. 1997.
[24] M. Dorigo, V. Maniezzo, and A. Colorni, "The ant system: optimization by a colony of cooperative agents," IEEE Trans. on System, Man, and Cybernetics, vol. 26, no. 1, pp. 1-13, Feb. 1996.
[25] A. Al-Ani, "Feature subset selection using ant colony optimization," Int. J. of Computational Intelligence, vol. 2, no. 1, pp. 53-58, 2005.
[26] T. Hiroyasu, M. Miki, Y. One, and Y. Minami, Ant Colony for Continuous Functions, the Science and Engineering, Doshisha University, 2000.
[27] ح. توحيدي، ح. نظام آبادي پور و س. سريزدي، "انتخاب ويژگي با استفاده از الگوريتم جمعيت مورچگان باينري،" اولين کنگره فازي و سيستم‌هاي هوشمند، دانشگاه فردوسي مشهد، ايران، صص. 269-275، 9-7 شهريور 1386.
[28] S. Kashef and H. Nezamabadi-pour, "A new feature selection algorithm based on binary ant colony optimization," in 5th Conf. on Information and Knowledge Technology, IKT2013, pp. 50-54, Shiraz, Iran, May 2013.
[29] N. Karaboga, A. Kalinli, and D. Karaboga, "Designing digital IIR filters using ant colony optimization algorithm," Engineering Applications of Artificial Intelligence, vol. 17, no. 3, pp. 301-309, Apr. 2004.
[30] L. Ozbakir, A. Baykasoglu, S. Kulluk, and H. Yapici, "TACO - miner: an ant colony based algorithm for rule extraction from trained neural networks," Expert Systems with Applications, vol. 36, no. 10, pp. 12295-12305, Dec. 2009.
[31] M. Janaki Meena, K. R. Chandran, A. Karthik, and A. Vijay Samuel, "An enhanced ACO algorithm to select features for text categorization and ites parallelization," Expert Systems with Applications, vol. 39, no. 5, pp. 5861-5871, Apr. 2012.
[32] UCI Machine Learning Repository, Center for Machine Learning and Intelligent Systems, http://archieve.ics.uci.edu/ml/datasets.html.
[33] L. Y. Chuang, C. H. Yang, and J. C. Li, "Chaotic maps based on binary particle swarm optimization for feature selection," Applied Soft Computing, vol. 11, no. 1, pp. 239-248, Jan. 2011.
[34] C. T. Su and H. C. Lin, "Applying electromagnetism-like mechanism for feature selection," Information Science, vol. 181, no. 5, pp. 972-986, Mar. 2011.
