صفوی، ع. ا.؛ پورجعفریان، ن.؛ صفوی، ع. (1393)، بهینهسازی بر پایۀ الگوریتمهای فراابتکاری، تهران: موسسۀ انتشاراتی پژوهشگران نشر دانشگاهی.
صنیعی آباده، م.؛ جبل عامیلیان، ز. (1392). الگوریتم های تکاملی و محاسبات زیستی، تهران: نیاز دانش.
Atashpaz-Gargari, E. & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. Evolutionary Computation (pp. 4661-4667). Singapore: IEEE.
Chmielewski, M., Grzymala-Busse, J., Peterson, N. & Than, S. (1993). The rule induction system LERS – a version for personal computer. Foundations of Computing and Decision Science, 18(3), 181-212.
Dash, M. & Liu, H. (2003). Consistency-based search in feature selection. Artificial Intelligence, 151(1), 155-176.
Guyon, I. & Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3(1), 1157-1182.
Hajihassani, M., Armaghani, D. J. & Marto, A. (2015). Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bulletin of Engineering Geology and the Environment, 74(3), 873-886.
Han, J. & Kamber, M. (2006). Data Mining: Concepts and Techniques. San Francisco: Diane Cerra.
Hu, Q., Xie, Z. & Yu, D. (2007). Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recognition, 40(12), 3509–3521.
Hu, Q., Yu, D. & Xie, Z. (2006). Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recognition Letters, 27(5), 414–423.
Hu, X. & Cercone, N. (1995). Learning in relational databases: a rough set approach. Computational Intelligence, 11(2), 323–338.
Ke, L., Zuren, F. & Zhigang, R. (2008). An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recognition Letters, 29(9), 1351-1357.
Kira, K. & Rendell, L. (1992). The feature selection problem: traditional methods and a new algorithm. AAAI, 2(1), 129–134.
Kohavi, R. & John, G. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(2), 273-324.
Kryszkiewicz, M. (1998). Rough setapproach to incomplete information systems. Information Sciences, 112(1), 39-49.
Lee, C. & Lee, G. (2006). Information gain and divergence-based feature selection for machine learning-based text categorization. Information Processing and Management, 42(1), 155-165.
Li, D., Zhang, B. & Leung, Y. (2004). On knowledge reduction in inconsistent decision information systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12(5), 651-672.
Liang, J. & Xu, Z. (2002). The algorithm on knowledge reduction in incomplete information systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(1), 95-103.
Liang, J., Chin, K., Dang, C. & YamRichid, C. (2002). A new method for measuring uncertainty and fuzziness in rough set theory. International Journal of General Systems, 31(4), 331-342.
Liang, J., Shi, Z., Li, D. & Wierman, M. (2006). Information entropy, rough entropy and knowledge granulation in incomplete information systems. International Journal of General Systems, 35(6), 641-654.
Lotfizadeh, L. A. (1992). Fuzzy logic, neural networks and soft computing. Communications of the ACM, 37(3), 77-84.
Meng, Z. & Shi, Z. (2009). A fast approach to attribute reduction in incomplete decision systems with tolerance relation-based rough sets. Information Sciences, 179(16), 2774–2793.
Mi, J., Wu, W. & Zhang, W. (2003). Comparative studies of knowledge reductions in inconsistent systems. Fuzzy systems and mathematics, 17(3), 54-60.
Modrzejewski, M. (1993, April). Feature selection using rough set theory. Machine Learning (pp. 213–226). Berlin Heidelberg: Springer.
Orlowska, E. & Pawlak, Z. (1984). Representation of nondeterministic information. Theoretical Computer Science, 29(1), 27-39.
Pawlak, Z. (1998). Rough set theory and its applications to data analysis. Cybernetics and Systems, 29(7), 662-668.
Pawlak, Z. & Skowron, A. (2007). Rudiments of rough sets. Information Sciences, 177(1), 3-27.
Qian, Y. & Liang, J. (2008). Combination entropy and combination granulation in rough set theory. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 16(2), 179-193.
Qian, Y., Liang, J. & Dang, C. (2008). Consistency measure, inclusion degree and fuzzy measure in decision tables. Fuzzy Sets and Systems, 159(18), 2353–2377.
Qian, Y., Liang, J. & Dang, C. (2008). Interval ordered information systems. Computers & Mathematics with Applications, 56(8), 1994–2009.
Qian, Y., Liang, J. & Wang, F. (2009). A new method for measuring the uncertainty in incomplete information systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 17(6), 855–880.
Qian, Y., Liang, J., Pedrycz, W. & Dang, C. (2011). An efficient accelerator for attribute reduction from incomplete data in rough set framework. Pattern Recognition, 44(8), 1658–1670.
Qiana, Y., Zhangb, H., Sangb, Y. & Lianga, J. (2014). Multigranulation decision-theoretic rough sets. International Journal of Approximate Reasoning, 55(1), 225-237.
Quinlan, J. (1986). Induction of decision trees. Machine Learning, 1(1), 81-106.
Safavi, A., Pourjafarian, N. & Safavi, A. (2014). Optimization Based On Meta Hueristic Algorithms. Shiraz: Researchers academic publishing. (in Persian)
Sanei Abadeh, M., & Jebel, Z. (2013). Evolutionary Algorithms and Biological Computing. Tehran: Niaz Danesh. (in Persian)
Shao, M. & Zhang, W. (2005). Dominance relation and rules in an incomplete ordered information system. International journal of intelligent systems, 20(1), 13-27.
Skowron, A. (1995). Extracting laws from decision tables: a rough setapproach. Computational Intelligence, 11(2), 371-388.
Slezak, D. (2002). Approximate entropy reducts. Fundamenta informaticae, 53(3), 365–390.
UCI. (2016). Retrieved from https://archive.ics.uci.edu/ml/datasets.html
Wang, G., Yu, H. & Yang, D. (2002). Decision table reduction based on conditional information entropy. Chinese Journal of Computer, 25(7), 759–766.
Wang, G., Zhao, J. & An, J. (2005). Acomparative study of algebra viewpoint and nattribute reduction. Foundamenta Informaticae, 68(3), 289–301.
Wu, S., Li, M., Huang, W., & Liu, S. (2004). An Improved Heuristic Algorithm of Attribute Reduction in Rough Set. Journal of Systems Science & Information, 2(3), 557-562.
Wu, W., Zhang, M., Li, H. & Mi, J. (2005). Knowledge reduction in random information systems via Dempster–Shafer theory of evidence. Information Sciences, 174(3), 143–164.
Yang, C. & Shu, L. (2006). Attribute reduction algorithm of incomplete decision table based on tolerance relation. Computer Technology and Development, 16(9).
Yu, J. (2005). General C-means clustering model. Pattern Analysis and Machine Intelligence, IEEE Transactions, 27(8), 1197-1211.