Abstract
Solving minimal attribute reduction (MAR) in rough set theory is a NP-hard and nonlinear constrained combinatorial optimization problem. Ant colony optimization (ACO), a new intelligent computing method, takes strategies of heuristic search, which is characterized by a distributed and positive feedback and it has the advantage of excellent global optimization ability for handling combinatorial optimization problems. Having considered that the existing information entropy and information gain methods fail to help to select the optimal minimal attribute every time, this paper proposed a novel attribute reduction algorithm based on ACO. Firstly, the algorithm adopts an improved information gain rate as heuristic information. Secondly, each ant solves a problem of minimum attributes reduction and then conduct redundancy test to each selected attribute. What’s more, redundant detection of all non-core attributes in the optimal solution will be perfomed in each generation. The result of the experiment on several datasets from UCI show that the proposed algorithms are more capable of finding the minimum attribute reduction and can faster converge and at the same time they can almost keep the classification accuracy, compared with the traditional attribute reduction based on ACO algorithm.
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References
Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)
Ding, H., Ding, S.F., Li-Hua, H.U.: Research progress of attribute reduction based on rough sets. Comput. Eng. Sci. 32(6), 92–94 (2010). (in Chinese)
Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Trans. Knowl. Data Eng. 16(12), 1457–1471 (2004)
Hedar, A.R., Wang, J., Fukushima, M.: Tabu search for attribute reduction in rough set theory. Soft. Comput. 12(9), 909–918 (2008)
Zhai, J.H., Liu, B., Zhang, S.: A feature selection approach based on rough set relative classification information entropy and particle swarm optimization. CAAI Trans. Intell. Syst. 12(3), 397–404 (2017). in Chinese
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man. Cybern. B 26(1), 29–41 (1996)
Liao, T., Stützle, T., Oca, M.A.M.D., et al.: A unified ant colony optimization algorithm for continuous optimization. Eur. J. Oper. Res. 234(3), 597–609 (2014)
Duan, H., Wang, D., Yu, X.: Review on research progress in ant colony algorithm. Chin. J. Nat. 28(2), 102–105 (2006). in Chinese
Miao, D., Wang, J.: Information representation of the concepts and operations in rough set theory. J. Softw. 10(2), 113–116 (1999). (in Chinese)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 331–362. Kluwer, Dordrecht (1992)
Yao, Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180(3), 341–353 (2010)
Lu, J., Li, D., Zhai, Y., et al.: A model for type-2 fuzzy rough sets. Inf. Sci. 328(C), 359–377 (2016). in Chinese
Qian, Y., Li, S., Liang, J., et al.: Pessimistic rough set based decisions: a multigranulation fusion strategy. Inf. Sci. 264(6), 196–210 (2014)
Wang, J., Miao, D.: Analysis on attribute reduction strategies of rough set. J. Comput. Sci. Technol. 13(2), 189–192 (1998). in Chinese
Wong, S.K.M., Ziarko, W.: On optimal decision rules in decision tables. Bull. Pol. Acad. Sci. Math. 33(11), 693–696 (1985)
Jensen, R., Shen, Q.: Finding rough set reducts with ant colony optimization. In: Proceedings of 2003 UK Workshop on Computational Intelligence, pp. 15–22 (2003)
Ke, L., Feng, Z., Ren, Z.: An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recogn. Lett. 29(9), 1351–1357 (2008)
Chen, Y., Chen, Y.: Attribute Reduction Algorithm Based on Information Entropy and Ant Colony Optimization. J. Chin. Comput. Syst. 36(3), 586–590 (2015). in Chinese
Chen, Y., Miao, D., Wang, R.: A rough set approach to feature selection based on ant colony optimization. Elsevier Science Inc. (2010)
Chebrolu, S., Sanjeevi, S.G.: Attribute reduction on continuous data in rough set theory using ant colony optimization metaheuristic. In: Proceedings of International Symposium on Women in Computing and Informatics. ACM, New York, pp. 17–24 (2015)
Yan, Y., Yang, H.: Knowledge reduction algorithm based on mutual information. J. Tsinghua Univ. 42(2), 1903–1906 (2007). in Chinese
Shannon, C.E., Weaver, W., Hajek, B., et al.: The mathematical theory of communication. Phys. Today 3(9), 31–32 (1950)
Hall, M., Frank, E., Holmes, G., et al.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Acknowledgements
This work has been supported by National Natural Science Foundation of China (61363029, 61572146, U1711263), Science Foundation of Guangxi Key Laboratory of Trusted Software (kx201515), and the Foundation of Guangxi Educational Committee (KY2015YB105).
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Wei, J., Wei, Q., Wen, Y. (2018). Attribute Reduction Algorithm Based on Improved Information Gain Rate and Ant Colony Optimization. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_12
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