کاهش ابعاد ویژگی مبتنی بر تئوری راف با استفاده از الگوریتم رقابت لیگ فوتبال
الموضوعات :
مجید عبدالرزاق نژاد
1
(دانشگاه بزرگمهر قائنات)
علی ادیبیان
2
(Bozorgmehr University of Qaenat)
الکلمات المفتاحية: داده‌کاویکاهش ویژگیانتخاب ویژگیتئوری مجموعه رافالگوریتم رقابت لیگ فوتبالطبقه‌بندی بیزین,
ملخص المقالة :
افزایش حجم پایگاه داده‌ها، مسئله کاهش ویژگی را ایجاد و پراهمیت می‌سازد. از این رو در علم داده‌کاوی، یکی از زیربخش‌های مهم، مسئله کاهش ابعاد (انتخاب ویژگی) می‌باشد که در آن زیرمجموعه‌ای از ویژگی‌هایی که بیشترین تأثیر را بر روی الگوهای پنهان داده اصلی دارند انتخاب می‌شوند. در سال‌های اخیر، تئوری مجموعه راف به عنوان یکی از ابزارهای موثر و کارآمد در کاهش ویژگی مورد توجه محققان قرار گرفته است. در این مقاله الگوریتم رقابت لیگ فوتبال برای اولین بار به منظور حل این مسئله مورد استفاده قرار گرفته شده است. توانایی فرار از بهین‌های موضعی، امکان بهره‌گیری از اطلاعات بازیکنان توزیع‌شده در فضای جستجو، همگرایی سریع به جواب‌های بهین و پارامترهای کم این الگوریتم، انگیزه این انتخاب بود. علاوه بر این نوآوری، ارائه تغییراتی همچون بهره‌گیری از مجموع توان بازیکنان ثابت و ذخیره در محاسبه توان هر تیم، در نظر گرفتن ترکیب ساختارهای پیوسته و گسسته برای هر بازیکن، ارائه یک مکانیزم ابتکاری گسسته‌سازی جدید، ارائه تحلیل هیدرولیک متناسب با مسئله تحقیق برای فرایند برازش هر بازیکن، ارائه اصلاح در عملگرهای تقلید و تهییج بر اساس چالش‌های نسخه اصلی را می‌توان به عنوان ایده‌های پیشنهادی به منظور اصلاح و سازگاری این الگوریتم برای مسئله کاهش ویژگی برشمرد. ایده‌های پیشنهادشده بر روی سه سطح داده در ابعاد کوچک، متوسط و بزرگ به همراه چهار الگوریتم فراابتکاری مشهور ژنتیک و بهینه‌سازی ازدحام ذارت، سیستم ایمنی مصنوعی و لیگ قهرمانان پیاده‌سازی گردیده و مقایسه نتایج به دست آمده نشان از مزیت‌های رقابتی قابل توجه الگوریتم رقابت لیگ فوتبال با سایر الگوریتم‌ها دارد.
[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.
