Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32734
A Fuzzy-Rough Feature Selection Based on Binary Shuffled Frog Leaping Algorithm

Authors: Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn


Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality. This paper presents a feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) of the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a modified version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The proposed feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Nine classifiers have been employed to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy.

Keywords: Binary shuffled frog leaping algorithm, feature selection, fuzzy-rough set, minimal reduct.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 670


[1] X. Zhao, D. Li, B. Yang, C. Ma, Y. Zhu, and H. Chen, “Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton,” Applied Soft Computing, vol. 24, pp. 585 – 596, 2014.
[2] E. Hancer, B. Xue, D. Karaboga, and M. Zhang, “A binary {ABC} algorithm based on advanced similarity scheme for feature selection,” Applied Soft Computing, vol. 36, pp. 334 – 348, 2015.
[3] N. Sreeja and A. Sankar, “Pattern matching based classification using ant colony optimization based feature selection,” Applied Soft Computing, vol. 31, pp. 91 – 102, 2015.
[4] S. Saha, R. Spandana, A. Ekbal, and S. Bandyopadhyay, “Simultaneous feature selection and symmetry based clustering using multiobjective framework,” Applied Soft Computing, vol. 29, pp. 479 – 486, 2015.
[5] X. Han, “Implicit feature selection for omics data phenotype discrimination,” Applied Soft Computing, vol. 20, pp. 70 – 82, 2014, hybrid intelligent methods for health technologies.
[6] S.-W. Lin, K.-C. Ying, C.-Y. Lee, and Z.-J. Lee, “An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection,” Applied Soft Computing, vol. 12, no. 10, pp. 3285 – 3290, 2012.
[7] A. M. Canuto, K. M. Vale, A. Feitos, and A. Signoretti, “Reinsel: A class-based mechanism for feature selection in ensemble of classifiers,” Applied Soft Computing, vol. 12, no. 8, pp. 2517 – 2529, 2012.
[8] K. Manimala, K. Selvi, and R. Ahila, “Hybrid soft computing techniques for feature selection and parameter optimization in power quality data mining,” Applied Soft Computing, vol. 11, no. 8, pp. 5485 – 5497, 2011.
[9] R. Nock and M. Sebban, “Sharper bounds for the hardness of prototype and feature selection,” in Algorithmic Learning Theory, ser. Lecture Notes in Computer Science, H. Arimura, S. Jain, and A. Sharma, Eds. Springer Berlin Heidelberg, 2000, vol. 1968, pp. 224–238.
[10] S. C. Yusta, “Different metaheuristic strategies to solve the feature selection problem,” Pattern Recognition Letters, vol. 30, no. 5, pp. 525 – 534, 2009.
[11] P. Pudil, J. Novoviov, and P. Somol, “Feature selection toolbox software package,” Pattern Recognition Letters, vol. 23, no. 4, pp. 487 – 492, 2002.
[12] M. ElAlami, “A filter model for feature subset selection based on genetic algorithm,” Knowledge-Based Systems, vol. 22, no. 5, pp. 356 – 362, 2009.
[13] S. Nemati, M. E. Basiri, N. Ghasem-Aghaee, and M. H. Aghdam, “A novel acoga hybrid algorithm for feature selection in protein function prediction,” Expert Systems with Applications, vol. 36, no. 10, pp. 12 086 – 12 094, 2009.
[14] S. M. Vieira, J. M. 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, 2010.
[15] M. Sebban and R. Nock, “A hybrid filter/wrapper approach of feature selection using information theory,” Pattern Recognition, vol. 35, no. 4, pp. 835 – 846, 2002.
[16] K. Thangavel and A. Pethalakshmi, “Dimensionality reduction based on rough set theory: A review,” Applied Soft Computing, vol. 9, no. 1, pp. 1 – 12, 2009.
[17] A. Verikas, M. Bacauskiene, D. Valincius, and A. Gelzinis, “Predictor output sensitivity and feature similarity-based feature selection,” Fuzzy Sets and Systems, vol. 159, no. 4, pp. 422 – 434, 2008.
[18] C. Degang and Z. Suyun, “Local reduction of decision system with fuzzy rough sets,” Fuzzy Sets and Systems, vol. 161, no. 13, pp. 1871 – 1883, 2010.
[19] R. Jensen and Q. Shen, “New approaches to fuzzy-rough feature selection,” Fuzzy Systems, IEEE Transactions on, vol. 17, no. 4, pp. 824–838, Aug 2009.
[20] Y. Chen, D. Miao, and R. Wang, “A rough set approach to feature selection based on ant colony optimization,” Pattern Recognition Letters, vol. 31, no. 3, pp. 226 – 233, 2010.
[21] N. Suguna and K. Thanushkodi, “A novel rough set reduct algorithm for medical domain based on bee colony optimization,” Journal of Computing, vol. 2, no. 6, pp. 49–54, June 2010.
[22] X. Wang, J. Yang, X. Teng, W. Xia, and R. Jensen, “Feature selection based on rough sets and particle swarm optimization,” Pattern Recognition Letters, vol. 28, no. 4, pp. 459 – 471, 2007.
[23] J. Wr´oblewski, “Finding minimal reducts using genetic algorithms,” in Proccedings of the second annual join conference on infromation science, 1995, pp. 186–189.
[24] J. R. Anaraki and M. Eftekhari, “Rough set based feature selection: A review,” in Information and Knowledge Technology (IKT), 2013 5th Conference on, May 2013, pp. 301–306.
[25] R. Jensen and Q. Shen, “Fuzzy-rough data reduction with ant colony optimization,” Fuzzy Sets and Systems, vol. 149, no. 1, pp. 5 – 20, 2005.
[26] J. Xiang, X. Han, F. Duan, Y. Qiang, X. Xiong, Y. Lan, and H. Chai, “A novel hybrid system for feature selection based on an improved gravitational search algorithm and k-nn method,” Applied Soft Computing, vol. 31, pp. 293 – 307, 2015.
[27] S. M. Vieira, L. F. Mendona, G. J. Farinha, and J. M. Sousa, “Modified binary {PSO} for feature selection using {SVM} applied to mortality prediction of septic patients,” Applied Soft Computing, vol. 13, no. 8, pp. 3494 – 3504, 2013.
[28] Z. Xu, G. Huang, K. Q. Weinberger, and A. X. Zheng, “Gradient boosted feature selection,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014, pp. 522–531.
[29] J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” Annals of statistics, pp. 1189–1232, 2001.
[30] L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and regression trees. CRC press, 1984.
[31] Z. Pawlak, “Rough sets,” International Journal of Computer & Information Sciences, vol. 11, no. 5, pp. 341–356, 1982.
[32] J. Komorowski, Z. Pawlak, L. Polkowski, and A. Skowron, “Rough sets: A tutorial,” in Rough-Fuzzy Hybridization: A New Trend in Decision Making, S. K. Pal and A. Skowron, Eds. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 1998, pp. 3–98.
[33] M. Eusuff, K. Lansey, and F. Pasha, “Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization,” Engineering Optimization, vol. 38, no. 2, pp. 129–154, 2006.
[34] Q. Duan, S. Sorooshian, and V. Gupta, “Effective and efficient global optimization for conceptual rainfall-runoff models,” Water Resources Research, vol. 28, no. 4, pp. 1015–1031, 1992.
[35] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Neural Networks, 1995. Proceedings., IEEE International Conference on, vol. 4, Nov 1995, pp. 1942–1948 vol.4.
[36] A. S. Reddy and K. Vaisakh, “Environmental constrained economic dispatch by modified shuffled frog leaping algorithm,” Journal of Bioinformatics and Intelligent Control, vol. 2, no. 3, pp. 216–222, 2013.
[37] A. M. Radzikowska and E. E. Kerre, “A comparative study of fuzzy rough sets,” Fuzzy Sets and Systems, vol. 126, no. 2, pp. 137 – 155, 2002.
[38] S. Kamyab, M. Eftekhari, and J. R. Anaraki, “A novel rough set based dissimilarity measure and its application in multimodal optimization,” in Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on, May 2012, pp. 180–185.
[39] R. Jensen and Q. Shen, Computational intelligence and feature selection: rough and fuzzy approaches. John Wiley & Sons, 2008, vol. 8.
[40] M. Lichman, “UCI machine learning repository,” 2013. (Online). Available:
[41] A. Tsanas, M. A. Little, C. Fox, and L. O. Ramig, “Objective automatic assessment of rehabilitative speech treatment in parkinson’s disease,” Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 22, no. 1, pp. 181–190, 2014.
[42] B. A. Johnson, “High-resolution urban land-cover classification using a competitive multi-scale object-based approach,” Remote Sensing Letters, vol. 4, no. 2, pp. 131–140, 2013.
[43] B. Johnson and Z. Xie, “Classifying a high resolution image of an urban area using super-object information,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 83, pp. 40–49, 2013.
[44] P. Van Der Putten and M. van Someren, “Coil challenge 2000: The insurance company case,” Published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report, vol. 9, pp. 1–43, 2000.
[45] I. Guyon, S. Gunn, A. Ben-Hur, and G. Dror, “Result analysis of the nips 2003 feature selection challenge,” in Advances in neural information processing systems, 2004, pp. 545–552.
[46] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The weka data mining software: An update,” SIGKDD Explor. Newsl., vol. 11, no. 1, pp. 10–18, Nov. 2009.
[47] K.-C. Wong, C.-H. Wu, R. K. Mok, C. Peng, and Z. Zhang, “Evolutionary multimodal optimization using the principle of locality,” Information Sciences, vol. 194, pp. 138 – 170, 2012.