Multimodal particle swarm optimization for feature selection

Applied Soft Computing - Tập 113 - Trang 107887 - 2021
Xiao-Min Hu1, Shou-Rong Zhang1, Min Li1,2, Jeremiah D. Deng3
1School of Computers, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
2School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
3Department of Information Science, University of Otago, Dunedin 9054, New Zealand

Tài liệu tham khảo

Gheyas, 2010, Feature subset selection in large dimensionality domains, Pattern Recognit., 43, 5, 10.1016/j.patcog.2009.06.009 Roberto, 2012, A global-ranking local feature selection method for text categorization, Expert Syst. Appl., 39, 12851, 10.1016/j.eswa.2012.05.008 Sadri, 2017, WN-Based approach to melanoma diagnosis from dermoscopy images, IET Image Process, 11, 475, 10.1049/iet-ipr.2016.0681 Pudil, 1994, Floating search methods in feature selection, Pattern Recognit. Lett., 15, 1119, 10.1016/0167-8655(94)90127-9 Calvet, 2017, Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs, Open Math., 15, 261, 10.1515/math-2017-0029 Choi, 2018, Efficient ranking and selection for stochastic simulation model based on hypothesis test, IEEE Trans. Syst. Man Cybern. Syst., 48, 1555, 10.1109/TSMC.2017.2679192 Kanan, 2008, GA-Based optimal selection of PZMI features for face recognition, Appl. Math. Comput., 205, 706, 10.1016/j.amc.2008.05.114 Hamdani, 2007, Multi-objective feature selection with NSGA II, vol. 4431, 240 Khushaba, 2011, Feature subset selection using differential evolution and a statistical repair mechanism, Expert Syst. Appl., 38, 11515, 10.1016/j.eswa.2011.03.028 Sameen, 2017, Integration of ant colony optimization and object-based analysis for LiDAR data classification, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 2055, 10.1109/JSTARS.2017.2650956 Fernandes, 2017, KANTS: a stigmergic ant algorithm for cluster analysis and swarm art, IEEE Trans. Cybern., 44, 843, 10.1109/TCYB.2013.2273495 Xue, 2013, Particle swarm optimization for feature selection in classification: A multi-objective approach, IEEE Trans. Cybern., 43, 1656, 10.1109/TSMCB.2012.2227469 Hu, 2020, Multiobjective particle swarm optimization for feature selection with fuzzy cost, IEEE Trans. Cybern., 50, 874 Abdollahzadeh, 2021, A multi-objective optimization algorithm for feature selection problems, Eng. Comput., 1 A.A. Bidgoli, H. Ebrahimpour-Komleh, S. Rahnamayan, A novel multi-objective binary differential evolution algorithm for multi-label feature selection, in: Proc. 2019 IEEE Congress on Evolutionary Computation (CEC), 2019, pp. 1588-1595. Y. Zhang, M. Rong, D. Gong, A multi-objective feature selection based on differential evolution, in: 2015 International Conference on Control, Automation and Information Sciences (ICCAIS), 2015, pp. 302-306. Zhang, 2017, Multi-objective particle swarm optimization approach for cost-based feature selection in classification, IEEE/ACM Trans. Comput. Biol. Bioinform., 14, 64, 10.1109/TCBB.2015.2476796 J. Kennedy, R.C. Eberhart, A discrete binary version of the particle swarm algorithm, in: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, Vol. 5, 1997, pp. 4104-4108. Chuang, 2011, Chaotic maps based on binary particle swarm optimization for feature selection, Appl. Soft Comput., 11, 239, 10.1016/j.asoc.2009.11.014 Blackwell, 2012, A study of collapse in bare bones particle swarm optimization, IEEE Trans. Evol. Comput., 16, 354, 10.1109/TEVC.2011.2136347 Zhang, 2014, Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis, Soft Comput., 18, 1337, 10.1007/s00500-013-1147-y C. Li, H. Hu, H. Gao, et al. Adaptive bare bones particle swarm optimization for feature selection, in: 2016 Chinese Control and Decision Conference (CCDC), 2016, pp. 1615-1620. Sakri, 2018, Particle swarm optimization feature selection for breast cancer recurrence prediction, IEEE Access, 6, 29637, 10.1109/ACCESS.2018.2843443 Nurhayati, F. Agustian, M.D.I. Lubis, Particle swarm optimization feature selection for breast cancer prediction, in: Proc. of 2020 8th International Conference on Cyber and IT Service Management (CITSM), Pangkal, Indonesia, 2020, pp. 1-6. Bayati, 2020, Mlpso: a filter multi-label feature selection based on particle swarm optimization, 1 Emary, 2016, Binary grey wolf optimization approaches for feature selection, Neurocomputing, 172, 371, 10.1016/j.neucom.2015.06.083 Al-Tashi, 2019, Binary optimization using hybrid grey wolf optimization for feature selection, IEEE Access, 7, 39496, 10.1109/ACCESS.2019.2906757 Hu, 2018, Feature selection for optimized high-dimensional biomedical data using an improved shuffled frog leaping algorithm, IEEE/ACM Trans. Comput. Biol. Bioinform., 15, 1765, 10.1109/TCBB.2016.2602263 A. Hammouri, Binary dragonfly algorithm for feature selection, in: 2017 International Conference on New Trends in Computing Sciences, 2017. Brezocnik, 2018, Swarm intelligence algorithms for feature selection: a review, Appl. Sci., 8, 10.3390/app8091521 Ma, 2018, Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey, Swarm Evol. Comput., 44, 365, 10.1016/j.swevo.2018.04.011 Li, 2017, Seeking multiple solutions: an updated survey on niching methods and their applications, IEEE Trans. Evol. Comput., 21, 518, 10.1109/TEVC.2016.2638437 Yang, 2017, Multimodal estimation of distribution algorithms, IEEE Trans. Cybern., 47, 636, 10.1109/TCYB.2016.2523000 Mahdaviani, 2015, Lade: learning automata based differential evolution, Int. J. Artif. Intell. Tools, 24, 10.1142/S0218213015500232 Hui, 2016, Ensemble and arithmetic recombination-based speciation differential evolution for multimodal optimization, IEEE Trans. Cybern., 46, 64, 10.1109/TCYB.2015.2394466 A. Tangherloni, L. Rundo, S. Spolaor, P. Cazzaniga, M.S. Nobile, GPU-powered multi-swarm parameter estimation of biological systems: a master–slave approach, in: Proc. 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), 2018, pp. 698-705. Yue, 2018, A multi-objective particle swarm optimizer using ring topology for solving multimodal multi-objective problems, IEEE Trans. Evol. Comput., 22, 805, 10.1109/TEVC.2017.2754271 Yang, 2017, Adaptive multimodal continuous ant colony optimization, IEEE Trans. Evol. Comput., 21, 10.1109/TEVC.2016.2591064 Qu, 2012, Differential evolution with neighborhood mutation for multimodal optimization, IEEE Trans. Evol. Comput., 16, 601, 10.1109/TEVC.2011.2161873 Li, 2013 Z.-G. Chen, Z.-H. Zhan, D. Liu, S. Kwong, J. Zhang, Particle swarm optimization with hybrid ring topology for multimodal optimization problems, in: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, Canada, 2020, 2020, pp. 2044-2049. J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proc. IEEE Int. Conf. Neural Netw., Vol. 4. 1995, pp. 1942-1948. L. Lv, Z. Chen, Z. Lu, A novel neural-network gradient optimization algorithm based on reinforcement learning, in: 2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 2019, pp. 106-111. Sun, 2019, Facilitating social collaboration in mobile cloud-based learning: a teamwork as a service (taas) approach, IEEE Trans. Learn. Technol., 7, 207, 10.1109/TLT.2014.2340402 S. Spolaor, A. Tangherloni, L. Rundo, M.S. Nobile, P. Cazzaniga, Reboot strategies in particle swarm optimization and their impact on parameter estimation of biochemical systems, in: Proc. 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2017, pp. 1-8. Yang, 2018, A level-based learning swarm optimizer for large-scale optimization, IEEE Trans. Evol. Comput., 22, 578, 10.1109/TEVC.2017.2743016 Engelbrecht, 2002 Cioppa, 2007, Where are the niches? dynamic fitness sharing, IEEE Trans. Evol. Comput., 11, 453, 10.1109/TEVC.2006.882433 R. Thomsen, Multimodal optimization using crowding-based differential evolution, in: Proc. IEEE Congr. Evol. Comput., 2. Portland, OR, USA, 2004, pp. 1382-1389. X. Li, Efficient differential evolution using speciation for multimodal function optimization, in: Proc. Genet. Evol. Comput. Conf., Washington, DC, USA, 2005, pp. 873-880. Shi. Cheng, Quande. Qin, Zhou. Wu, et al. Multimodal optimization using particle swarm optimization algorithms: CEC 2015 competition on single objective multi-niche optimization, in: 2015 IEEE Congress on Evolutionary Computation (CEC), 2015, pp. 1075-1082. Li, 2010, Niching without niching parameters: Particle swarm optimization using a ring topology, IEEE Trans. Evol. Comput., 14, 150, 10.1109/TEVC.2009.2026270 Qu, 2012, Niching particle swarm optimization with local search for multimodal optimization, Inform. Sci., 197, 131, 10.1016/j.ins.2012.02.011 P.M. Murphy, D.W. Aha, UCI Repository of Machine Learning Databases, Tech. Rep., Dept. Inf. Comput. Sci. University of California, Irvine, CA, USA, [Online]. Available: http://www.ics.uci.edu/mlearn/MLRepository.html. Chang, 2011, LIBSVM: a library for support vector machines, ACM Trans. Intell. Syst. Technol. (TIST), 2, 1