Abstract
Multi-Objective Particle Swarm Optimization (MOPSO) for feature selection has attracted increasing attention of researchers recently. However, in the existing methods, quick convergence usually degrades the diversity of the population, especially when many irrelevant and redundant features involved in them. To this end, a diversity based competitive multi-objective particle swarm optimization for feature selection problem (named D-CMOPSO) is proposed. In D-CMOPSO, a diversified competition based learning mechanism is proposed to improve the quality of found feature subset, which consists of three parts: exemplar particle construction, pairwise competition, and diversified learning strategy. The proposed competition mechanism utilizes the above three parts to boost the diversity in the following generations. Moreover, in order to guide the initial population to evolve the promising area, a maximal information coefficient based initialization strategy is also suggested. The experimental results demonstrate that the proposed D-CMOPSO is competitive for feature selection problem.
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References
Agor, J., Özaltın, O.Y.: Feature selection for classification models via bilevel optimization. Comput. Oper. Res. 106, 156–168 (2019)
Khammassi, C., Krichen, S.: A GA-LR wrapper approach for feature selection in network intrusion detection. Comput. Secur. 70, 255–277 (2017)
Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Cybern. 43(6), 1656–1671 (2013)
Zhang, Y., Gong, D.W., Cheng, J.: Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(1), 64–75 (2017)
Lac, H.C., Stacey, D.A.: Feature subset selection via multi-objective genetic algorithm. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, vol. 3, pp. 1349–1354. IEEE (2005)
Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)
Brezočnik, L., Fister, I., Podgorelec, V.: Swarm intelligence algorithms for feature selection: a review. Appl. Sci. 8(9), 1521 (2018)
Nguyen, H.B., Xue, B., Liu, I., Andreae, P., Zhang, M.: New mechanism for archive maintenance in PSO-based multi-objective feature selection. Soft. Comput. 20(10), 3927–3946 (2016)
Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2015)
Zhang, X., Zheng, X., Ran, C., Qiu, J., Jin, Y.: A competitive mechanism based multi-objective particle swarm optimizer with fast convergence. Inf. Sci. 427, 63–76 (2018)
Reshef, D.N., et al.: Detecting novel associations in large data sets. Science 334(6062), 1518–1524 (2011)
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical recipes in C. Art Sci. Comput. 10(1), 176–177 (1995)
Gutlein, M., Frank, E., Hall, M., Karwath, A.: Large-scale attribute selection using wrappers. In: IEEE Symposium on Computational Intelligence and Data Mining, pp. 332–339 (2009)
Xue, B., Zhang, M., Browne, W.N.: New fitness functions in binary particle swarm optimisation for feature selection. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8 (2012)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2000)
Zhang, X., Tian, Y., Cheng, R., Jin, Y.: A decision variable clustering based evolutionary algorithm for large-scale many-objective optimization. IEEE Trans. Evol. Comput. 22(1), 97–112 (2018)
UCI machine learning repository. https://archive.ics.uci.edu/ml/. Accessed 30 Mar 2019
Acknowledgment
This work is supported by the Anhui University College Students Innovation and Entrepreneurship Training Program, Natural Science Foundation of China (Grant No. 61876184 and 61822301), the Key Program of Natural Science Project of Educational Com-mission of Anhui Province (Grant No. KJ2017A013) and the Academic and Technology Leader Imported Project of Anhui University (No. J01006057). This work was also supported in part by the Natural Science Foundation of Anhui Province (Grant No. 1708085MF166, No. 1908085MF219, and No. 1908085QF271), Humanities and Social Sciences Project of Chinese Ministry of Education (Grant No. 18YJC870004). Provincial Quality Engineering Project of Anhui Province (Grant No. 2017jyxm0086).
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Qiu, J., Cheng, F., Zhang, L., Xu, Y. (2019). A Diversity Based Competitive Multi-objective PSO for Feature Selection. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_3
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DOI: https://doi.org/10.1007/978-3-030-26969-2_3
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