A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information

2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Volumn , Issue , 2015, Pages 2420-2427

  Hancer, Emrah ^a,b   Xue, Bing ^b   Zhang, Mengjie ^b   Karaboga, Dervis ^a   Akay, Bahriye ^a  

^a ERCIYES UNIVERSITY   (Turkey)

^b VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

EVOLUTIONARY ALGORITHMS; FEATURE EXTRACTION; FUZZY FILTERS; OPTIMIZATION;

CLASSIFICATION ACCURACY; CONFLICTING OBJECTIVES; EVALUATION CRITERIA; FEATURE SELECTION PROBLEM; FITNESS EVALUATIONS; MULTI OBJECTIVE ALGORITHM; MULTI-OBJECTIVE ARTIFICIAL BEE COLONIES; MUTUAL INFORMATIONS;

CLASSIFICATION (OF INFORMATION);

EID: 84963614582     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2015.7257185     Document Type: Conference Paper

References (36)

1. M. Dash and H. Liu, "Feature selection for classification," Intelligent Data Analysis, vol. 1, no. 14, pp. 131-156, 1997.
2. B. Chakraborty, "Genetic algorithm with fuzzy fitness function for feature selection," in Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE'2002), vol. 1, 2002, pp. 315-319 vol.1.
3. K. Neshatian and M. Zhang, "Pareto front feature selection: Using genetic programming to explore feature space," in 11th Annual Conference on Genetic and Evolutionary Computation, 2009, pp. 1027-1034.
4. R. Jensen, "Performing feature selection with ACO," in Swarm Intelligence in Data Mining, ser. Studies in Computational Intelligence. Springer Berlin Heidelberg, 2006, vol. 34, pp. 45-73.
5. X. Wang, J. Yang, X. Teng, W. Xia, and R. Jensen, "Feature selection based on rough sets and particle swarm optimization," Pattern Recognition Letters, vol. 28, no. 4, pp. 459-471, 2007.
6. B. Xue, M. Zhang, and W. Browne, "Novel initialisation and updating mechanisms in PSO for feature selection in classification," in Applications of Evolutionary Computation, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013, vol. 7835, pp. 428-438.
7. L. Cervante, B. Xue, L. Shang, and M. Zhang, "A dimension reduction approach to classification based on particle swarm optimisation and rough set theory," in AI 2012: Advances in Artificial Intelligence. Springer, 2012, pp. 313-325.
8. M. Schiezaro and H. Pedrini, "Data feature selection based on artificial bee colony algorithm," EURASIP Journal on Image and Video Processing, vol. 2013, no. 1, pp. 1-8, 2013.
9. B. Xue, L. Cervante, L. Shang, and M. Zhang, "A particle swarm optimisation based multi-objective filter approach to feature selection for classification," in PRICAI, ser. Lecture Notes in Computer Science, 2012, vol. 7458, pp. 673-685.
10. B. Xue, L. Cervante, L. Shang, W. Browne, and M. Zhang, "A multi-objective PSO for filter-based feature selection in classification problems," Connection Science, vol. 24, no. 2-3, pp. 91-116, 2012.
11. D. Karaboga, B. Gorkemli, C. Ozturk, and N. Karaboga, "A comprehensive survey: artificial bee colony ABC algorithm and applications," Artificial Intelligence Review, vol. 42, no. 1, pp. 21-57, 2014.
12. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: Nsga-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002.
13. D. Karaboga, "Artificial bee colony algorithm," Scholarpedia, vol. 5, no. 3, p. 6915, 2010.
14. R. Khushaba, A. Al-Jumaily, and A. Al-Ani, "Novel feature extraction method based on fuzzy entropy and wavelet packet transform for myoelectric control," in International Symposium on Communications and Information Technologies (ISCIT'07), 2007, pp. 352-357.
15. R. Khushaba, S. Kodagoda, S. Lal, and G. Dissanayake, "Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm," IEEE Transactions on Biomedical Engineering, vol. 58, no. 1, pp. 121-131, 2011.
16. M. A. Hall, "Correlation-based feature selection for machine learning," Ph.D. dissertation, The University of Waikato, 1999.
17. K. Kira and L. A. Rendell, "A practical approach to feature selection," in Proceedings of the Ninth International Workshop on Machine Learning, ser. ML92. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1992, pp. 249-256.
18. H. Almuallim and T. G. Dietterich, "Learning boolean concepts in the presence of many irrelevant features," Artificial Intelligence, vol. 69, pp. 279-305, 1994.
19. R. Battiti, "Using mutual information for selecting features in supervised neural net learning," IEEE Transactions on Neural Networks, vol. 5, no. 4, pp. 537-550, 1994.
20. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information: criteria of max-dependency, max-relevance, and minredundancy," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226-1238, 2005.
21. H. H. Yang and J. Moody, "Data visualization and feature selection: New algorithms for nongaussian data," in in Advances in Neural Information Processing Systems. mit Press, 1999, pp. 687-693.
22. N. Kwak and C.-H. Choi, "Input feature selection for classification problems," IEEE Transactions on Neural Networks, vol. 13, no. 1, pp. 143-159, 2002.
23. D. Yu, S. An, and Q. Hu, "Fuzzy mutual information based minredundancy and max-relevance heterogeneous feature selection," International Journal of Computational Intelligence Systems, vol. 4, no. 4, pp. 619-633, 2011.
24. L. Cervante, B. Xue, M. Zhang, and L. Shang, "Binary particle swarm optimisation for feature selection: A filter based approach," in IEEE Congress on Evolutionary Computation (CEC'2012), 2012, pp. 1-8.
25. B. Xue, L. Cervante, L. Shang, W. N. Browne, and M. Zhang, "Multiobjective evolutionary algorithms for filter based feature selection in classification," International Journal on Artificial Intelligence Tools, vol. 22, no. 04, p. 1350024, 2013.
26. -, "Binary PSO and rough set theory for feature selection: A multiobjective filter based approach," International Journal of Computational Intelligence and Applications, vol. 13, no. 02, p. 1450009, 2014.
27. X. Li, "A non-dominated sorting particle swarm optimizer for multiobjective optimization," in Genetic and Evolutionary Computation Conference, 2003, vol. 2723, pp. 37-48.
28. M. R. Sierra and C. A. C. Coello, "Improving pso-based multi-objective optimization using crowding, mutation and epsilon-dominance," in Evolutionary Multi-Criterion Optimization, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005, vol. 3410, pp. 505-519.
29. N. Suguna and K. G. Thanushkodi, "An independent rough set approach hybrid with artificial bee colony algorithm for dimensionality reduction," American Journal of Applied Sciences, vol. 8, no. 3, pp. 261-266, 2011.
30. R. B. Agrawal, K. Deb, K. Deb, and R. B. Agrawal, "Simulated binary crossover for continuous search space," Tech. Rep., 1994.
31. K. Liagkouras and K. Metaxiotis, "An elitist polynomial mutation operator for improved performance of moeas in computer networks," in 22nd International Conference on Computer Communications and Networks (ICCCN'2013), 2013, pp. 1-5.
32. X. Zou, M. Liu, L. Kang, and J. He, "A high performance multiobjective evolutionary algorithm based on the principles of thermodynamics," in Parallel Problem Solving from Nature-PPSN VIII, ser. Lecture Notes in Computer Science, 2004, vol. 3242, pp. 922-931.
33. B. Akay, "Synchronous and asynchronous pareto-based multi-objective artificial bee colony algorithms," Journal of Global Optimization, vol. 57, no. 2, pp. 415-445, 2013.
34. L. D. Landau and E. M. Lifshitz, Statistical Physics. Course of Theoretical Physics 5, 3rd ed. Oxford: Pergamon Press, 1980.
35. K. Bache and M. Lichman, "UCI machine learning repository," 2013. [Online]. Available: http://archive.ics.uci.edu/ml
36. B. Xue, Z. Mengjie, and W. N. Browne, "Particle swarm optimization for feature selection in classification: A multi-objective approach," IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 1656-1671, 2013.