Multi-objective particle swarm optimisation (PSO) for feature selection

GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation

Volumn , Issue , 2012, Pages 81-88

  Xue, Bing ^a   Zhang, Mengjie ^a   Browne, Will N ^a  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

feature selection; multi objective optimisation; particle swarm optimisation

Indexed keywords

CLASSIFICATION ERRORS; CLASSIFICATION PERFORMANCE; CONVENTIONAL METHODS; DATA PREPROCESSING TECHNIQUE; DATA SETS; FEATURE SUBSET; MULTI OBJECTIVE; MULTI OBJECTIVE ALGORITHM; MULTI-OBJECTIVE GENETIC ALGORITHM; NON-DOMINATED SORTING; NONDOMINATED SOLUTIONS; PARETO FRONT; PARTICLE SWARM OPTIMISATION; SINGLE OBJECTIVE;

FEATURE EXTRACTION; MULTIOBJECTIVE OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO);

ALGORITHMS;

EID: 84864657940     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2330163.2330175     Document Type: Conference Paper

References (27)

1. H. Almuallim and T. G. Dietterich. Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69:279-305, 1994.
2. G. Azevedo, G. Cavalcanti, and E. Filho. An approach to feature selection for keystroke dynamics systems based on pso and feature weighting. In IEEE Congress on Evolutionary Computation (CEC'07), pages 3577-3584, 2007.
3. B. Chakraborty. Genetic algorithm with fuzzy fitness function for feature selection. In IEEE International Symposium on Industrial Electronics (ISIE'02), volume 1, pages 315- 319, 2002.
4. B. Chakraborty. Feature subset selection by particle swarm optimization with fuzzy fitness function. In 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE'08), volume 1, pages 1038-1042, 2008.
5. M. Dash and H. Liu. Feature selection for classification. Intelligent Data Analysis, 1(4):131-156, 1997.
6. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182-197, 2002.
7. A. Frank and A. Asuncion. UCI machine learning repository, 2010.
8. M. Gutlein, E. Frank, M. Hall, and A. Karwath. Large-scale attribute selection using wrappers. In IEEE Symposium on Computational Intelligence and Data Mining (CIDM '09), pages 332-339, 2009.
9. C. L. Huang and J. F. Dun. A distributed pso-svm hybrid system with feature selection and parameter optimization. Application on Soft Computing, 8:1381-1391, 2008.
10. J. Kennedy and R. Eberhart. Particle swarm optimization. In IEEE International Conference on Neural Networks, volume 4, pages 1942-1948, 1995.
11. J. Kennedy and R. Eberhart. A discrete binary version of the particle swarm algorithm. In IEEE International Conference on Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., volume 5, pages 4104-4108, 1997.
12. J. Kennedy and W. Spears. Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. In IEEE Congress on Evolutionary Computation (CEC'98), pages 78-83, 1998.
13. K. Kira and L. A. Rendell. A practical approach to feature selection. Assorted Conferences and Workshops, pages 249-256, 1992.
14. R. Kohavi and G. H. John. Wrappers for feature subset selection. Artificial Intelligence, 97:273-324, 1997.
15. X. Li. A non-dominated sorting particle swarm optimizer for multiobjective optimization. In GECCO, pages 37-48, 2003.
16. Y. Liu, G. Wang, H. Chen, and H. Dong. An improved particle swarm optimization for feature selection. Journal of Bionic Engineering, 8(2):191-200, 2011.
17. T. Marill and D. Green. On the effectiveness of receptors in recognition systems. IEEE Transactions on Information Theory, 9(1):11-17, 1963.
18. H. Ming. A rough set based hybrid method to feature selection. In International Symposium on Knowledge Acquisition and Modeling (KAM '08), pages 585-588, 2008.
19. A. Mohemmed, M. Zhang, and M. Johnston. Particle swarm optimization based adaboost for face detection. In IEEE Congress on Evolutionary Computation (CEC'09), pages 2494-2501, 2009.
20. K. Neshatian and M. Zhang. Dimensionality reduction in face detection: A genetic programming approach. In 24th International Conference Image and Vision Computing New Zealand (IVCNZ'09), pages 391-396, 2009.
21. Y. Shi and R. Eberhart. A modified particle swarm optimizer. In IEEE International Conference on Evolutionary Computation (CEC'98), pages 69-73, 1998.
22. M. R. Sierra and C. A. C. Coello. Improving pso-based multi-objective optimization using crowding, mutation and epsilon-dominance. In EMO, pages 505-519, 2005.
23. A. Unler and A. Murat. A discrete particle swarm optimization method for feature selection in binary classification problems. European Journal of Operational Research, 206(3):528-539, 2010.
24. F. Van Den Bergh. An analysis of particle swarm optimizers. PhD thesis, Pretoria, South Africa, 2002.
25. A. Whitney. A direct method of nonparametric measurement selection. IEEE Transactions on Computers, C-20(9):1100-1103, 1971.
26. C. S. Yang, L. Y. Chuang, C. H. Ke, and C. H. Yang. Boolean binary particle swarm optimization for feature selection. In IEEE Congress on Evolutionary Computation (CEC'08), pages 2093-2098, 2008.
27. H. Yuan, S. S. Tseng, and W. Gangshan. A two-phase feature selection method using both filter and wrapper. In IEEE International Conference on Systems, Man, and Cybernetics (SMC'99), volume 2, pages 132-136, 1999.