Feature selection algorithm based on bare bones particle swarm optimization

Neurocomputing

Volumn 148, Issue , 2015, Pages 150-157

  Zhang, Yong ^a   Gong, Dunwei ^a   Hu, Ying ^a   Zhang, Wanqiu ^a  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

Author keywords

1 Nearest neighbor; Bare bones particle swarm; Feature selection; Reinforced memory; Uniform combination

Indexed keywords

1-NEAREST NEIGHBOR; BARE-BONES PARTICLE SWARM OPTIMIZATIONS; FEATURE SELECTION ALGORITHM; PARTICLE SWARM; UNIFORM COMBINATION;

FEATURE EXTRACTION;

ALGORITHM; ARTICLE; BARE BONES PARTICLE SWARM OPTIMIZATION; CLASSIFICATION; COMPARATIVE STUDY; FEATURE SELECTION; MACHINE LEARNING; MATHEMATICAL COMPUTING; MEASUREMENT ACCURACY; PERFORMANCE MEASUREMENT SYSTEM; VALIDATION PROCESS;

EID: 84908072362     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.09.049     Document Type: Article

References (31)

1. Su C.T., Lin H.C. Applying electromagnetism-like mechanism for feature selection. Inf. Sci. 2011, 181(5):972-986.
2. A.E. Mohamed, G. Gilles, S. Yahya, Adaptive particle swarm optimizer for feature selection, in: Proceedings of the Eleventh International Conference on Intelligent Data Engineering and Automated Learning. Lecture Notes in Computer Science 6283, 2010, pp. 226-233.
3. Somol P., Pudil P., Kittler J. Fast branch and bound algorithms for optimal feature selection. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26(7):900-912.
4. Yang J., Olafsson S. Optimization-based feature selection with adaptive instance sampling. Comput. Oper. Res. 2006, 33(11):3088-3106.
5. Oh I.S., Lee J.S., Moon B.R. Hybrid genetic algorithms for feature selection. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26(1):1424-1437.
6. Huang C.L. ACO-based hybrid classification system with feature subset selection and model parameters optimization. Neurocomputing 2009, 73(1-3):438-448.
7. Lin S.W., Lee Z.J., Chen S.C., Tseng T.Y. Parameter determination of support vector machine and feature selection using simulated annealing approach. Appl. Soft Comput. 2008, 8(4):1505-1512.
8. J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.
9. Wang X., Yang J., Teng X., Xia W., Jensen R. Feature selection based onrough sets and particle swarm optimization. Pattern Recognit. Lett. 2007, 28(4):459-471.
10. E.G. Talbi, L. Jourdan, J. Garcia-Nieto, E. Alba, Comparison of population based metaheuristics for feature selection: application to microarray data classification, in: Proceedings of AICCSA, 2008.
11. Chuang L.Y., Yang C.H., Li J.C. Chaotic maps based on binary particle swarm optimization for feature selection. Appl. Soft Comput. 2011, 11(1):239-248.
12. Alper U., Alper M. A discrete particle swarm optimization method for feature selection in binary classification problems. Eur. J. Oper. Res. 2010, 206:528-539.
13. Chuang L.Y., Yang C.S., Wu K.C., Yang C.H. Gene selection and classification using Taguchi chaotic binary particle swarm optimization. Expert Syst. Appl. 2011, 38:13367-13377.
14. Tripathi P.K., Bandyopadhyay S., Pal S.K. Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Inf. Sci. 2007, 177(22):5033-5049.
15. J. Kennedy, Bare bones particle swarms, in: Proceeding of the 2003 IEEE Swarm Intelligence Symposium, 2003, pp. 80-87.
16. M.G.H. Omran, A. Engelbrecht, A. Salman, Barebones particle swarm for integer programming problems, in: Proceeding of the 2007 IEEE Swarm Intelligence Symposium, 2007, pp. 170-175.
17. Zhang H., Kennedy D.D., Rangaiah G.P., et al. Novel bare bones particle swarm optimization and its performance for modeling vapor-liquid equilibrium data. Fluid Phase Equilib. 2010, 301:33-45.
18. Zhang Y., Gong D.W., Ding Z.H. Barebones multi-objective particle swarm optimization for environmental/economic dispatch. Inf. Sci. 2012, 192(1):213-227.
19. Y.H. Shi, R. C. Eberhart, A modied particle swarm optimizer, in: Proceeding of IEEE Congress on Evolutionary Computation, 1998, pp. 303-308.
20. J. Kennedy, R.C. Eberhart, A discrete binary version of the particle swarm algorithm, in: Proceedings of 1997 Conference Systems Man and Cybernetics, 1997, pp. 4104-4108.
21. F. Pan, X. Hu, R. Eberhart, Y. Chen, An analysis of bare bones particle swarm, in: Proceeding of the 2008 IEEE Swarm Intelligence Symposium, September 2008, pp. 21-23.
22. E. Fix, J.L. Hodges, Discriminatory Analysis-nonparametric Discrimination: Consistency Properties. Project 21-49-004, Report 4, US Air Force School of Aviation Medicine, Randolph Field, 1951, pp. 261-279.
23. P.M. Murphy, D.W. Aha, UCI Repository of Machine Learning Databases. Technical report, Department of Information and Computer Science, University of California, Irvine, California. Available at: http://www.ics.uci.edu/~mlearn/~MLRepository.html.
24. Chuang L.Y., Tsai S.W., Yang C.H. Improved binary particle swarm optimization using catfish effect for feature selection. Expert Syst. Appl. 2011, 38:12699-12707.
25. Clerc M., Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 2002, 6(1):58-72.
26. Van den Bergh F., Engelbrecht A. A study of particle swarm optimization particle trajectories. Inf. Sci. 2006, 176(8):937-971.
27. Cooren Y., Clerc M., Siarry P. MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm. Comput. Optim. Appl. 2011, 49(2):379-400.
28. Pan Q.K., Tasgetiren M.F., Liang Y.C. A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem. Comput. Oper. Res. 2008, 35(9):2807-2839.
29. Pan Q.K., Wang L. No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm. Int. J. Adv. Manuf. Technol. 2008, 39(7-8):796-807.
30. Gong Y.J., Zhang J., Liu Q., et al. Optimizing the vehicle routing problem with time windows. a discrete particle swarm optimization approach. IEEE Trans. Syst., Man Cybern. C: Appl. Rev. 2012, 42(2):254-267.
31. Mann H.B., Whitney D.R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 1947, 18(1):50-60.