Fuzzy-Rough Bireducts Algorithm Based on Particle Swarm Optimization

doi:10.13190/j.jbupt.2020-237

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (4): 49-55.doi: 10.13190/j.jbupt.2020-237

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Fuzzy-Rough Bireducts Algorithm Based on Particle Swarm Optimization

LIU Zhan-feng, PAN Su

Jiangsu Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Received:2020-11-23 Published:2021-07-13

Abstract

Abstract: Selecting informative features and removing noise instances are beneficial to gain a clean dataset and promote the performance of subsequent classifiers. A novel algorithm for fuzzy-rough bireducts with particle swarm optimization is proposed. The fitness function with ε-bireduct is employed to evaluate the candidate fuzzy-rough bireducts, which drives the particle swarm optimization search process toward better candidate solutions. The selected optimal bireduct is utilized to construct the subsequent classifier. The experimental results show that the proposed algorithm is superior to the counterpart, which reduces the instances and features effectively, and obtains high-quality bireducts. The classification accuracy of the proposed algorithm is thus better than the counterpart.

Key words: fuzzy-rough, feature selection, instance selection, particle swarm optimization

CLC Number:

TP181

LIU Zhan-feng, PAN Su. Fuzzy-Rough Bireducts Algorithm Based on Particle Swarm Optimization[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4): 49-55.

References

[1] Hancer E, Xue B, Zhang M. A survey on feature selection approaches for clustering[J]. Artificial Intelligence Review, 2020, 53(2):4519-4545.
[2] 张东方, 陈海燕, 王建东. 半监督特征选择综述[J/OL]. 计算机应用研究, 2021, 38(2)[2020-10-13]. http://www.arocmag.com/article/02-2021-02-003.html.
[3] Derrac J, García S, Herrera F, et al. A survey on evolutionary instance selection and generation[J]. International Journal of Applied Metaheuristic Computing, 2017, 1(1):60-92.
[4] Ślęzak D, Janusz A. Ensembles of bireducts:towards robust classification and simple representation[C]//International Conference on Future Generation Information Technology. Berlin:Springer, 2011:64-77.
[5] Pawlak Z. Rough sets:theoretical aspects of reasoning about data[M].[S. l.]:Kluwer Academic Publishers, 1992.
[6] Jensen R, Shen Q. Computational intelligence and feature selection:rough and fuzzy approaches[J]. Kybernetes, 2008, 38(3/4):438.
[7] Mac Parthalain N, Jensen R. Simultaneous feature and instance selection using fuzzy-rough bireducts[C]//IEEE International Conference on Fuzzy Systems.[S.l.]:IEEE, 2013:1-8.
[8] Mac Parthalain N, Jensen R, Diao R. Fuzzy-rough set bireducts for data reduction[J]. IEEE Transactions on Fuzzy Systems, 2019(99):1-1.
[9] Diao R, Mac Parthalain N, Jensen R, et al. Heuristic search for fuzzy-rough bireducts and its use in classifier ensembles[C]//2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).[S. l.]:IEEE, 2014:1504-1511.
[10] Ammar M, Bouaziz S, Alimi A M, et al. Hybrid harmony search algorithm for global optimization[C]//Nature and Biologically Inspired Computing, 2013 World Congress on.[S. l.]:IEEE, 2013.
[11] Kennedy J, Eberhart R. Particle swarm optimization[C]//Proceedings of ICNN'95-International Conference on Neural Networks.[S. l.]:IEEE, 1995:1942-1948.
[12] Tu C J, Chuang L Y, Chang J Y, et al. Feature selection using PSO-SVM[J]. IAENG International Journal of Computer Science, 2007, 33(1):111-116.
[13] Wang X, Yang J, Teng X, et al. Feature selection based on rough sets and particle swarm optimization[J]. Pattern Recognition Letters, 2007, 28(4):459-471.
[14] Mistry K, Zhang L, Neoh S C, et al. A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition[J]. IEEE Transactions on Cybernetics, 2016, 47(6):1496-1509.
[15] 孙艺, 宋振铭, 赵佳琪. 粒子群算法在金融风险模型中的研究与改进[J]. 吉林大学学报(信息科学版), 2020, 38(2):85-91. Sun Yi, Song Zhenming, Zhao Jiaqi. Research and improvement of particle swarm optimization in financial risk model[J]. Journal of Jilin University (Information Science Edition), 2020, 38(2):85-91.
[16] Tran B, Xue B, Zhang M. A new representation in PSO for discretization-based feature selection[J]. IEEE Transactions on Cybernetics, 2017, 48(6):1733-1746.
[17] Dubois D, Prade H. Putting rough sets and fuzzy sets together[M]//Intelligent Decision Support. Dordrecht:Springer, 1992:203-232.
[18] Jensen R, Shen Q. New approaches to fuzzy-rough feature selection[J]. IEEE Transactions on fuzzy systems, 2008, 17(4):824-838.
[19] Stawicki S, Ślęzak D. Recent advances in decision bireducts:complexity, heuristics and streams[C]//International Conference on Rough Sets and Knowledge Technology. Heidelberg:Springer, 2013:200-212.
[20] Shi Y, Eberhart R C. Empirical study of particle swarm optimization[C]//Proceedings of the 1999 Congress on Evolutionary Computation-CEC99(Cat. No.99TH8406).[S.l.]:IEEE, 1999:1945-1950.

Fuzzy-Rough Bireducts Algorithm Based on Particle Swarm Optimization

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