Attribute Reduction Based on Rough Set Theory by Soccer League Competition Algorithm
Subject Areas : electrical and computer engineering
M. Abdolrazzagh-Nezhad
1
*
,
Ali Adibiyan
2
1 - دانشگاه بزرگمهر قائنات
2 -
Abstract :
Increasing the dimension of the databases have involved the attribute reduction as a critical issue in data mining that it searches to find a subset of attributes with the most effectiveness on the hidden patterns. In the current years, the rough set theory has been considered by researchers as one of the most effective and efficient tools to the reduction. In this paper, the soccer league competition algorithm is modified and adopted to solve the attribute reduction problem for the first time. The ability to escape the local optimal, the ability to use the information distributed by players in the search space, the rapid convergence to the optimal solutions, and the low algorithm’s parameters were the motivation of considering the algorithm in the current research. The proposed ideas to modify the algorithm consist of utilizing the total power of fixed and saved players in calculating the power of each team, considering the combination of continuous and discrete structures for each player, proposing a novel discretization method, providing a hydraulic analysis appropriate to the research problem for evaluating each player, designing correction in Imitation and Provocation operators based on the challenges in their original version. The proposed ideas are performed on small, medium and large data sets from UCI and the experimental results are compared with the state-of-the-art algorithms. This comparison shows that the competitive advantages of the proposed algorithm over the investigated algorithms.
[1] C. Sima and E. R. Dougherty, "The peaking phenomenon in the presence of feature-selection," Pattern Recognition Letters, vol. 29, no. 11, pp. 1667-1674, Aug. 2008.
[2] Z. Pawlak, "Rough sets," International J. of Computer & Information Sciences, vol. 11, no. 5, pp. 341-356, Oct. 1982.
[3] M. Abdolrazzagh-Nezhad, H. Radgohar, and S. N. Salimian, "Enhanced cultural algorithm to solve multi-objective attribute reduction based on rough set theory," Mathematics and Computers in Simulation, vol. 170, pp. 332-350, Apr. 2020.
[4] M. Ghanei Ostad, H. Khosravi Mahmoee, and M. Abdolrazzagh Nezhad, "Attribute reduction in incomplete information system based on rough set theory using fuzzy imperialist competitive algorithm," J. of Information Technology Management, vol. 9, no. 1, pp. 123-142, Spring 2017.
[5] J. H. Dai and Y. X. Li, Heuristic Genetic Algorithm for Minimal Reduction Decision System Based on Rough Set Theory, 2002.
[6] C. Huang, T. Li, and T. Peng, "A hybrid approach of rough set theory and genetic algorithm for fault diagnosis," The International J. of Advanced Manufacturing Technology, vol. 27, no. 1/2, pp. 119-127, Jan. 2005.
[7] Y. G. Ren, Y. Wang, and D. Q. Yan, "Rough set attribute reduction algorithm based on GA," Mini-Micro System, vol. 27, no. 5, pp. 862-865, Mar. 2006.
[8] N. S. Jaddi and S. Abdullah, "Hybrid of genetic algorithm and great deluge algorithm for rough set attribute reduction," Turkish J. of Electrical Engineering & Computer Sciences, vol. 21, no. 6, pp. 1737-1750, 2013.
[9] S. Y. Jing, "A hybrid genetic algorithm for feature subset selection in rough set theory," Soft Computing, vol. 18, no. 7, pp. 1373-1382, Oct. 2014.
[10] Z. Xu, D. Gu, and B. Yang, Attribute Reduction Algorithm Based on Genetic Algorithm, 2009.
[11] A. K. Das, S. Sengupta, and S. Bhattacharyya, "A group incremental feature selection for classification using rough set theory based genetic algorithm," Applied Soft Computing, vol. 65, pp. 400-411, Apr. 2018.
[12] R. Jensen and Q. Shen, "Finding rough set reducts with ant colony optimization," in Proc. UK Workshop on Computational Intelligence, 8 pp., 2003.
[13] L. Ke, Z. Feng, and Z. Ren, "An efficient ant colony optimization approach to attribute reduction in rough set theory," Pattern Recognition Letters, vol. 29, no. 9, pp. 1351-1357, Jul. 2008.
[14] T. Q. Deng, et al., "An improved ant colony optimization applied to attributes reduction," in Proc. Fuzzy Information and Engineering, vol 54, 6 pp., Springer, Berlin, 2009.
[15] 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, Feb. 2010.
[16] M. M. Kabir, M. Shahjahan, and K. Murase, "A new hybrid ant colony optimization algorithm for feature selection," Expert Systems with Applications, vol. 39, no. 3, pp. 3747-3763, Feb. 2012.
[17] X. Wang, et al., "Feature selection based on rough sets and particle swarm optimization," Pattern Recognition Letters, vol. 28, no. 4, pp. 459-471, Mar. 2007.
[18] D. Ye, Z. Chen, and J. Liao, "A new algorithm for minimum attribute reduction based on binary particle swarm optimization with vaccination," in Pacific-Asia Conf. on Knowledge Discovery and Data Mining, PAKDD'07, pp. 1029-1036, May 2007.
[19] L. Cervante, B. Xue, L. Shang, and M. Zhang, "Binary particle swarm optimisation and rough set theory for dimension reduction in classification," in Proc. IEEE Congress on Evolutionary Computation, CEC’13, pp. 2428-2435, Cancun, Mexico, 20-23 Jun. 2013.
[20] J. Dai, et al., "Discrete particle swarm optimization approach for cost sensitive attribute reduction," Knowledge-Based Systems, vol. 102, 116-126, Jun. 2016.
[21] N. Suguna and K. Thanushkodi, A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization, arXiv preprint arXiv:1006.4540, Jun. 2010.
[22] D. Ye and Z. Chen, "A new approach to minimum attribute reduction based on discrete artificial bee colony," Soft Computing, vol. 19, no. 7, pp. 1893-1903, Jul. 2015.
[23] J. Cai, et al., "An improved artificial bee colony algorithm for minimal time cost reduction," International J. of Machine Learning and Cybernetics, vol. 5, no. 5, pp. 743-752, Dec. 2014.
[24] J. Ganesan, et al., "Tolerance rough set firefly-based quick reduct," Neural Computing and Applications, vol. 28, no. 10, pp. 2995-3008, Aug. 2017.
[25] X. Y. Luan, Z. P. Li, and T. Z. Liu, "A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm," Neurocomputing, vol. 174, pt. A, pp. 522-529, Jan. 2016.
[26] R. Cheruku, et al., "Rst-batminer: a fuzzy rule miner integrating rough set feature selection and bat optimization for detection of diabetes disease," Applied Soft Computing, vol. 67, pp. 764-780, Jun. 2018.
[27] W. Ding, et al., "Enhanced minimum attribute reduction based on quantum-inspired shuffled frog leaping algorithm," J. of Systems Engineering and Electronics, vol. 24, no. 3, pp. 426-434, Jun. 2013.
[28] M. A. El Aziz and A. E. Hassanien, "An improved social spider optimization algorithm based on rough sets for solving minimum number attribute reduction problem," Neural Computing and Applications, vol. 30, no. 8, pp. 2441-2452, Jan. 2018.
[29] A. E. Hassanien, et al., "An improved moth flame optimization algorithm based on rough sets for tomato diseases detection," Computers and Electronics in Agriculture, vol. 136, pp. 86-96, Apr. 2017.
[30] A. E. Hassanien, R. M. Rizk-Allah, and M. Elhoseny, "A hybrid crow search algorithm based on rough searching scheme for solving engineering optimization problems," J. of Ambient Intelligence and Humanized Computing, 25 pp., 2018.
[31] S. Abdullah, L. Golafshan, and M. Z. A. Nazri, "Re-heat simulated annealing algorithm for rough set attribute reduction," International J. of Physical Sciences, vol. 6, no. 8, pp. 2083-2089, 2011.
[32] M. Abdolrazzagh-Nezhad and S. Izadpanah, "A modified electromagnetic-like mechanism for rough set attribute reduction," in Proc. Int. Conf. on Information and Software Technologies, ICIST’16, pp. 356-368, Sept. 2016.
[33] A. R. Hedar, J. Wang, and M. Fukushima, "Tabu search for attribute reduction in rough set theory," Soft Computing, vol. 12, no. 9, pp. 909-918, Nov. 2008.
[34] 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, Feb. 2010.
[35] N. Moosavian and B. K. Roodsari, "Soccer league competition algorithm: a novel meta-heuristic algorithm for optimal design of water distribution networks," Swarm and Evolutionary Computation, vol. 17, pp. 14-24, Aug. 2014.
[36] A. H. Kashan, "League Championship Algorithm (LCA): an algorithm for global optimization inspired by sport championships," Applied Soft Computing, vol. 16, pp. 171-200, Mar. 2014.
[37] G. Dudek, "An artificial immune system for classification with local feature selection," IEEE Trans. on Evolutionary Computation, vol. 16, no. 6, pp. 847-860, Dec. 2012.
[38] N. S. Hoa and N. H. Son, "Some efficient algorithms for rough set methods," in Proc. IPMU, pp. 1451-1456, Granada, Spain, 1-5 Jul. 1996.
[39] K. Deb, Multi-Objective Optimization, in Search Methodologies, Springer, pp. 403-449, 2014.
[40] J. Han, J. Pei, and M. Kamber, Data Mining: Concepts and Techniques, Elsevier, 2011.