PSO and statistical clustering for feature selection: A new representation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8886, Issue , 2014, Pages 569-581

  Nguyen, Hoai Bach ^a   Xue, Bing ^a   Zhang, Mengjie ^a   Liu, Ivy ^a  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

Classification; Feature selection; Particle swarm optimisation; Representation

Indexed keywords

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

CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; CONVENTIONAL METHODS; FEATURE SELECTION ALGORITHM; FEATURE SELECTION PROBLEM; PARTICLE SWARM OPTIMISATION; REPRESENTATION; STATISTICAL CLUSTERING;

CLASSIFICATION (OF INFORMATION);

EID: 84921327742     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-13563-2_48     Document Type: Article

References (31)

1. Al-Ani, A., Alsukker, A., Khushaba, R.N.: Feature subset selection using differential evolution and a wheel based search strategy. Swarm and Evolutionary Computation 9, 15-26 (2013)
2. Asuncion, A., Newman, D.: Uci machine learning repository (2007)
3. Bach, F.R., Jordan, M.I.: A probabilistic interpretation of canonical correlation analysis. Tech. rep. (2005)
4. Cervante, L., Xue, B., Shang, L., Zhang, M.: Binary particle swarm optimisation and rough set theory for dimension reduction in classification. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2428-2435 (2013)
5. Cervante, L., Xue, B., Shang, L., Zhang, M.: A multi-objective feature selection approach based on binary pso and rough set theory. In: Middendorf, M., Blum, C. (eds.) EvoCOP 2013. LNCS, vol. 7832, pp. 25-36. Springer, Heidelberg (2013)
6. Cervante, L., Xue, B., Zhang, M., Shang, L.: Binary particle swarm optimisation for feature selection: A filter based approach. In: IEEE Congress on Evolutionary Computation (CEC 2012), pp. 881-888 (2012)
7. Chuang, L.Y., Tsai, S.W., Yang, C.H.: Improved binary particle swarm optimization using catfish effect for feature selection. Expert Systems with Applications 38, 12699-12707 (2011)
8. Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1(4), 131-156 (1997)
9. Engelbrecht, A.P.: Computational intelligence: an introduction, 2nd edn. Wiley (2007)
10. Fdhila, R., Hamdani, T.M., Alimi, A.M.: Distributed mopso with a new population subdivision technique for the feature selection. In: International Symposium on Computational Intelligence and Intelligent Informatics (ISCIII 2011), pp. 81-86 (2011)
11. Gutlein, M., Frank, E., Hall, M., Karwath, A.: Large-scale attribute selection using wrappers. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2009), pp. 332-339. IEEE (2009)
12. Kanan, H.R., Faez, K.: An improved feature selection method based on ant colony optimization (aco) evaluated on face recognition system. Applied Mathematics and Computation 205(2), 716-725 (2008)
13. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948 (1995)
14. Marill, T., Green, D.: On the effectiveness of receptors in recognition systems. IEEE Transactions on Information Theory 9(1), 11-17 (1963)
15. Matechou, E., Liu, I., Pledger, S., Arnold, R.: Biclustering models for ordinal data. Presentation at the NZ Statistical Assn. Annual Conference, University of Auckland (2011)
16. Neshatian, K., Zhang, M.: Genetic programming for feature subset ranking in binary classification problems. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 121-132. Springer, Heidelberg (2009)
17. Pledger, S., Arnold, R.: Multivariate methods using mixtures: correspondence analysis, scaling and pattern detection. Computational Statistics and Data Analysis (2013), http://dx.doi.org/10.1016/j.csda.2013.05.013
18. Pudil, P., Novovicova, J., Kittler, J.V.: Floating search methods in feature selection. Pattern Recognition Letters 15(11), 1119-1125 (1994)
19. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation (CEC 1998), pp. 69-73 (1998)
20. Stearns, S.: On selecting features for pattern classifier. In: Proceedings of the 3rd International Conference on Pattern Recognition, pp. 71-75. IEEE Press, Coronado (1976)
21. Van Den Bergh, F.: An analysis of particle swarm optimizers. Ph.D. thesis, University of Pretoria (2006)
22. Whitney, A.: A direct method of nonparametric measurement selection. IEEE Transactions on Computers C-20(9), 1100-1103 (1971)
23. nd edn. Morgan Kaufmann (2005)
24. Xue, B., Cervante, L., Shang, L., Browne, W.N., Zhang, M.: A multi-objective particle swarm optimisation for filter based feature selection in classification problems. Connection Science 24(2-3), 91-116 (2012)
25. Xue, B., Cervante, L., Shang, L., Browne, W.N., Zhang, M.: Binary PSO and rough set theory for feature selection: A multi-objective filter based approach. International Journal of Computational Intelligence and Applications 13(02), 1450009 (2014)
26. Xue, B., Zhang, M., Browne, W.N.: Multi-objective particle swarm optimisation (PSO) for feature selection. In: Genetic and Evolutionary Computation Conference (GECCO 2012), Philadelphia, PA, USA, pp. 81-88. ACM (2012)
27. Xue, B., Zhang, M., Browne, W.N.: New fitness functions in binary particle swarm optimization for feature selection. In: IEEE CEC 2012, pp. 2145-2152 (2012)
28. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: A multi-objective approach. IEEE Transactions on Cybernetics 43(6), 1656-1671 (2013)
29. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms. Applied Soft Computing 18, 261-276 (2014)
30. Yang, C.S., Chuang, L.Y., Li, J.C.: Chaotic maps in binary particle swarm optimization for feature selection. In: IEEE Conference on Soft Computing in Industrial Applications (SMCIA 2008), pp. 107-112 (2008)
31. Zhu, Z.X., Ong, Y.S., Dash, M.: Wrapper-filter feature selection algorithm using a memetic framework. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 37(1), 70-76 (2007)