Single feature ranking and binary particle swarm optimisation based feature subset ranking for feature selection

Conferences in Research and Practice in Information Technology Series

Volumn 122, Issue , 2012, Pages 27-36

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

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

Feature selection; Feature subset ranking; Particle swarm optimisation; Single feature ranking

Indexed keywords

BINARY PARTICLE SWARM; CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; COMPLEMENTARY FEATURES; CROSS VALIDATION; DATA SETS; FEATURE RANKING; FEATURE SUBSET; FORWARD SELECTION; K NEAREST NEIGHBOURS (K-NN); PARTICLE SWARM OPTIMISATION; WRAPPER-BASED FEATURE SELECTION;

CLASSIFICATION (OF INFORMATION); COMPUTER SCIENCE; DATA PROCESSING; FEATURE EXTRACTION; PARTICLE SWARM OPTIMIZATION (PSO);

SET THEORY;

EID: 84868684316     PISSN: 14451336     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Abeel, T., de Peer, Y. V. & Saeys, Y. (2009 ), 'Java-ML: A Machine Learning Library', Journal of Machine Learning Research 10, 931-934
2. Alba, E., Garcia-Nieto, J., Jourdan, L. & Talbi, E.G. (2007), Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms, in 'IEEE Congress on Evolutionary Computation', pp. 284-290.
3. Azevedo, G.L.F., Cavalcanti, G.D.C. & Filho, E.C.B. (2007), An approach to feature selection for keystroke dynamics systems based on PSO and feature weighting, in 'IEEE Congress on Evolutionary Computation', pp. 3577-3584.
4. Caruana, R. & Freitag, D. (1994), Greedy Attribute Selection, in 'In Proceedings of International Conference on Machine Learning', pp. 28-36.
5. Chakraborty, B. (2008), Feature subset selection by particle swarm optimization with fuzzy fitness function, in '3rd International Conference on Intelligent System and Knowledge Engineering', Vol. 1, pp. 1038-1042.
6. Chuang, L.Y., Chang, H.W., Tu, C.J.& Yang, C.H. (2008 ), 'Improved binary PSO for feature selection using gene expression data', Computational Biology and Chemistry 32(29), 29-38.
7. Clerc, M. & Kennedy, J. (2002), The particle swarm - explosion, stability, and convergence in a multidimensional complex space, in 'IEEE Congress on Evolutionary Computation', Vol. 6, pp. 58-73. (Pubitemid 34404564)
8. Dash, M. & Liu, H. (1997), 'Feature selection for classification', Intelligent Data Analysis 1, 131-156.
9. Frank, A. & Asuncion, A. (2010), UCI Machine Learning Repository, [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
10. Gutlein, M., Frank, E., Hall, M. & Karwath, A. (2009), Large-scale attribute selection using wrappers, in 'IEEE Symposium on Computational Intelligence and Data Mining', pp. 332-339.
11. Guyon, I., Elisseeff, A. & Liu, H. (2003), 'An introduction to variable and feature selection', The Journal of Machine Learning Research 3, 1157-1182.
12. Huang, C.J. & Dun, J.F. (2008), 'A distributed PSOSVM hybrid system with feature selection and parameter optimization', Applied Soft Computing 8(4), 1381-1391.
13. Kennedy, J. & Eberhart, R. (1995), Particle swarm optimization, in 'IEEE International Conference on Neural Networks',Vol. 4, pp. 1942-1948.
14. Kennedy, J. & Eberhart, R. (1997), A discrete binary version of the particle swarm algorithm, in 'IEEE International Conference on Systems, Man, and Cybernetics',Vol. 5, pp. 4104-4108.
15. Kennedy, J. & Spears, W.M. (1998), 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',pp. 78-83.
16. Kohavi, R. & John, G.H. (1997), 'Wrappers for feature subset selection', Artificial Intelligence 97, 315-333.
17. Langley, P. (1994), Selection of relevant features in machine learning, in 'Proceedings of the AAAI Fall symposium on relevance',pp. 127-131.
18. Liu, Y.N., Wang, G., Chen, H.L., & Dong, H. (2011), 'An Improved Particle Swarm Optimization for Feature Selection', Journal of Bionic Engineering 8(2), 191-200.
19. Marill, T., & Green, D.M. (1963), 'On the effectiveness of receptors in recognition systems', IEEE Transactions on Information Theory 9(1), 11-17.
20. Neshatian, K. & Zhang, M.J. (2009), Genetic Programming for Feature Subset Ranking in Binary Classification Problems, in 'European Conference on Genetic Programming', pp. 121-132.
21. Talbi, E.G., Jourdan, L., Garcia-Nieto, J. & Alba, E. (2008), Comparison of population based metaheuristics for feature selection: Application to microarray data classification, in 'ACS/IEEE International Conference on Computer Systems and Applications',pp. 45-52.
22. Unler, A.& Murat, A. (2010), 'A discrete particle swarm optimization method for feature selection in binary classification problems', European Journal of Operational Research 206, 528-539.
23. Van Den Bergh, F. (2002), An analysis of particle swarm optimizers, Ph.D., University of Pretoria, South Africa.
24. Wang, X.Y., Yang, J., Teng, X.L. & Xia, W.J. (2007), 'Feature selection based on rough sets and particle swarm optimization', Pattern Recognition Letters 28(4), 459-471. (Pubitemid 44920436)
25. Whitney, A.W. (2007), 'A direct method of nonparametric measurement selection', IEEE Transactions on Computers 20(4), 1100-1103.
26. Witten I.H. & Frank E. (2005), Data Mining: Practical Machine Learning Tools and Techniques., 2nd Edition, Morgan Kaufmann, San Francisco.
27. Yang, C.S., Chuang, L.Y. & Ke, C.H. (2008), Boolean binary particle swarm optimization for feature selection, in 'IEEE Congress on Evolutionary Computation', pp. 2093-2098.
28. Yang, C.S., Chuang, L.Y. & Li, J.C. (2008), Chaotic maps in binary particle swarm optimization for feature selection, in 'IEEE Conference on Soft Computing in Industrial Applications', pp. 107-112.