Since datasets are often collected for purposes other than data mining, they typically contain numerous redundant and irrelevant features. The presence of such features can pose challenges for learning systems, including increased computational costs and reduced accuracy. Consequently, feature selection as a preprocessing step can enhance system performance in applications that utilize datasets. This paper presents a method that employs rough set theory as a criterion for evaluating the quality of a feature subset and the intelligent water drops algorithm as a search method. The primary objective of this study is to reduce the computational complexity associated with applying rough set theory in feature selection while improving the quality of the final solution set through the use of the intelligent water drops search algorithm. The proposed method has been tested on multiple datasets from the University of California, Irvine (UCI) repository, and its results have been compared with existing similar methods. The findings demonstrate that eliminating redundant computations can significantly reduce the time complexity of rough set-based feature selection and that the intelligent water drops algorithm serves as an efficient search strategy for feature selection.
Kabir, M. M., Shahjahan, M., & Murase, K. (2011). A new local search based hybrid genetic algorithm for feature selection. Neurocomputing, 74(17), 2914-2928. https://doi.org/10.1016/j.neucom.2011.03.034
Das, S. (2001). Filters, wrappers and a boosting-based hybrid for feature selection. In Proceedings of the Eighteenth International Conference on Machine Learning (pp. 74-81).
Fleuret, F. (2004). Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research, 5(Nov), 1531-1555.
Kudo, M., & Sklansky, J. (2000). Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33(1), 25-41. https://doi.org/10.1016/S0031-3203(99)00041-2
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3(Mar), 1157-1182.
Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5), 341-356. https://doi.org/10.1007/BF01001956
Suguna, N., & Thanushkodi, K. (2010). A novel rough set reduct algorithm for medical domain based on bee colony optimization.
Jensen, R., & Shen, Q. (2003). Finding rough set reducts with ant colony optimization. In Proceedings of the 2003 UK Workshop on Computational Intelligence (pp. 15-22).
Shah-Hosseini, H. (2007). Problem solving by intelligent water drops. In 2007 IEEE Congress on Evolutionary Computation (pp. 3226-3231). https://doi.org/10.1109/CEC.2007.4424885
Shah-Hosseini, H. (2008). Intelligent water drops algorithm: A new optimization method for solving the multiple knapsack problem. International Journal of Intelligent Computing and Cybernetics, 1(2), 193-212. https://doi.org/10.1108/17563780810874717
Shah-Hosseini, H. (2009). The intelligent water drops algorithm: A nature-inspired swarm-based optimization algorithm. International Journal of Bio-Inspired Computation, 1(1/2), 71-79. https://doi.org/10.1504/IJBIC.2009.022775
Niu, S. H., Ong, S. K., & Nee, A. Y. C. (2012). An improved intelligent water drops algorithm for achieving optimal job-shop scheduling solutions. International Journal of Production Research, 50(15), 4192-4205. https://doi.org/10.1080/00207543.2011.600346
Shah-Hosseini, H. (2012). Intelligent water drops algorithm for automatic multilevel thresholding of grey-level images using a modified Otsu's criterion. International Journal of Modelling, Identification and Control, 15(4), 241-251. https://doi.org/10.1504/IJMIC.2012.046402
Modrzejewski, M. (1993). Feature selection using rough sets theory. In Proceedings of the European Conference on Machine Learning (pp. 213-226). https://doi.org/10.1007/3-540-56602-3_138
Zhong, N., Dong, J., & Ohsuga, S. (2001). Using rough sets with heuristics for feature selection. Journal of Intelligent Information Systems, 16(3), 199-214.
Chouchoulas, A., & Shen, Q. (2001). Rough set-aided keyword reduction for text categorization. Applied Artificial Intelligence, 15(9), 843-873. https://doi.org/10.1080/088395101753210773
Jensen, R., & Shen, Q. (2004). Fuzzy-rough attribute reduction with application to web categorization. Fuzzy Sets and Systems, 141(3), 469-485. https://doi.org/10.1016/S0165-0114(03)00021-6
Mac Parthalain, N., Shen, Q., & Jensen, R. (2007). Distance measure assisted rough set feature selection. In 2007 IEEE International Fuzzy Systems Conference (pp. 1-6). https://doi.org/10.1109/FUZZY.2007.4295518
Peng, R., Hao, T., & Fang, Y. (2021). Syntax-aware neural machine translation directed by syntactic dependency degree. Neural Computing and Applications, 33, 16609-16625. https://doi.org/10.1007/s00521-021-06256-4
Sowkuntla, P., & Prasad, P. (2021). MapReduce-based parallel fuzzy-rough attribute reduction using a discernibility matrix. Applied Intelligence, 52, 154-173. https://doi.org/10.1007/s10489-021-02253-1
Jensen, R., & Shen, Q. (2008). Computational intelligence and feature selection: Rough and fuzzy approaches. Wiley. https://doi.org/10.1002/9780470377888
Wróblewski, J. (1995). Finding minimal reducts using genetic algorithms. In Proceedings of the Second Annual Joint Conference on Information Sciences (pp. 186-189).
Zhai, L.-Y., Khoo, L.-P., & Fok, S.-C. (2002). Feature extraction using rough set theory and genetic algorithms An application for the simplification of product quality evaluation. Computers & Industrial Engineering, 43(4), 661-676. https://doi.org/10.1016/S0360-8352(02)00131-6
Hedar, A., Omar, M. A., & Sewisy, A. A. (2015). Rough sets attribute reduction using an accelerated genetic algorithm. In 16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (1-7). https://doi.org/10.1109/SNPD.2015.7176207
Ke, L., Feng, Z., & Ren, Z. (2008). An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recognition Letters, 29(9), 1351-1357. https://doi.org/10.1016/j.patrec.2008.02.006
Chen, Y., Miao, D., & Wang, R. (2010). A rough set approach to feature selection based on ant colony optimization. Pattern Recognition Letters, 31(3), 226-233. https://doi.org/10.1016/j.patrec.2009.10.013
Forsati, R., Moayedikia, A., Jensen, R., Shamsfard, M., & Meybodi, M. R. (2014). Enriched ant colony optimization and its application in feature selection. Neurocomputing, 142, 354-371. https://doi.org/10.1016/j.neucom.2014.03.053
Wang, X., Yang, J., Teng, X., Xia, W., & Jensen, R. (2007). Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters, 28(4), 459-471. https://doi.org/10.1016/j.patrec.2006.09.003
Bae, C., Yeh, W. C., Chung, Y. Y., & Liu, S. L. (2010). Feature selection with intelligent dynamic swarm and rough set. Expert Systems with Applications, 37(10), 7026-7032. https://doi.org/10.1016/j.eswa.2010.03.016
Hedar, A. R., Wang, J., & Fukushima, M. (2008). Tabu search for attribute reduction in rough set theory. Soft Computing, 12(9), 909-918. https://doi.org/10.1007/s00500-007-0260-1
Alijla, B. O., Peng, L. C., Khader, A. T., & Al-Betar, M. A. (2013). Intelligent water drops algorithm for rough set feature selection. In Lecture Notes in Computer Science (LNCS, vol. 7803, Part 2) (pp. 356-365). https://doi.org/10.1007/978-3-642-36543-0_37
Su, Y., & Guo, J. (2017). A novel strategy for minimum attribute reduction based on rough set theory and fish swarm algorithm. Computational Intelligence and Neuroscience, 2017, 1-7. https://doi.org/10.1155/2017/6573623
Wang, F., Xu, J., & Li, L. (2014). A novel rough set reduct algorithm to feature selection based on artificial fish swarm algorithm. In Springer, Cham (pp. 24-33). https://doi.org/10.1007/978-3-319-11897-0_4
Hoa, N. S., & Hoa, N. S. (1996). Some efficient algorithms for rough set methods. In Proceedings of IPMU'96 Granada, Spain (pp. 1541-1457).
Ioerger, T. R. (2002). Instance-based filter for feature selection. Journal of Machine Learning Research, 1, 1-23.
Dalvand,E. and Bafandkar,M. (2021). Feature Selection Method Based on the Rough Set Theory and the Intelligent Water Drops Algorithm. Transactions on Machine Intelligence, 4(3), 137-152. doi: 10.47176/TMI.2021.137
MLA
Dalvand,E. , and Bafandkar,M. . "Feature Selection Method Based on the Rough Set Theory and the Intelligent Water Drops Algorithm", Transactions on Machine Intelligence, 4, 3, 2021, 137-152. doi: 10.47176/TMI.2021.137
HARVARD
Dalvand E., Bafandkar M. (2021). 'Feature Selection Method Based on the Rough Set Theory and the Intelligent Water Drops Algorithm', Transactions on Machine Intelligence, 4(3), pp. 137-152. doi: 10.47176/TMI.2021.137
CHICAGO
E. Dalvand and M. Bafandkar, "Feature Selection Method Based on the Rough Set Theory and the Intelligent Water Drops Algorithm," Transactions on Machine Intelligence, 4 3 (2021): 137-152, doi: 10.47176/TMI.2021.137
VANCOUVER
Dalvand E., Bafandkar M. Feature Selection Method Based on the Rough Set Theory and the Intelligent Water Drops Algorithm. Trans. Mach. Intell., 2021; 4(3): 137-152. doi: 10.47176/TMI.2021.137
