A Survey on Evolutionary Computation Approaches to Feature Selection

IEEE Transactions on Evolutionary Computation

Volumn 20, Issue 4, 2016, Pages 606-626

  Xue, Bing ^a   Zhang, Mengjie ^a   Browne, Will N ^a   Yao, Xin ^b  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Classification; data mining; evolutionary computation; feature selection; machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; CALCULATIONS; CLASSIFICATION (OF INFORMATION); DATA MINING; EVOLUTIONARY ALGORITHMS; LEARNING SYSTEMS; SURVEYS;

CLASSIFICATION ALGORITHM; FEATURE SELECTION PROBLEM; IMPROVING PERFORMANCE; ISSUES AND CHALLENGES; LOST OPPORTUNITIES; SEARCH SPACES; STATE OF THE ART;

FEATURE EXTRACTION;

EID: 84982830440     PISSN: 1089778X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TEVC.2015.2504420     Document Type: Article

References (250)

1. M. Dash and H. Liu, "Feature selection for classification," Intell. Data Anal., vol. 1, nos. 1-4, pp. 131-156, 1997.
2. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol. 3, pp. 1157-1182, Mar. 2003.
3. A. S. U. Kamath, K. De Jong, and A. Shehu, "Effective automated feature construction and selection for classification of biological sequences," PLoS One, vol. 9, no. 7, 2014, Art. ID e99982.
4. W. A. Albukhanajer, Y. Jin, J. A. Briffa, and G. Williams, "Evolutionary multi-objective optimization of trace transform for invariant feature extraction," in Proc. IEEE Congr. Evol. Comput. (CEC), Brisbane, QLD, Australia, 2012, pp. 1-8.
5. W. A. Albukhanajer, J. A. Briffa, and Y. Jin, "Evolutionary multiobjective image feature extraction in the presence of noise," IEEE Trans. Cybern., vol. 45, no. 9, pp. 1757-1768, Sep. 2015.
6. Y. Liu, F. Tang, and Z. Zeng, "Feature selection based on dependency margin," IEEE Trans. Cybern., vol. 45, no. 6, pp. 1209-1221, Jun. 2015.
7. H. Liu and Z. Zhao, "Manipulating data and dimension reduction methods: Feature selection," in Encyclopedia of Complexity and Systems Science. Berlin, Germany: Springer, 2009, pp. 5348-5359.
8. H. Liu, H. Motoda, R. Setiono, and Z. Zhao, "Feature selection: An ever evolving frontier in data mining," in Proc. JMLR Feature Sel. Data Min., vol. 10. Hyderabad, India, 2010, pp. 4-13.
9. H. Liu and L. Yu, "Toward integrating feature selection algorithms for classification and clustering," IEEE Trans. Knowl. Data Eng., vol. 17, no. 4, pp. 491-502, Apr. 2005.
10. A. Unler and A. Murat, "A discrete particle swarm optimization method for feature selection in binary classification problems," Eur. J. Oper. Res., vol. 206, no. 3, pp. 528-539, 2010.
11. Y. Liu et al., "An improved particle swarm optimization for feature selection," J. Bionic Eng., vol. 8, no. 2, pp. 191-200, 2011.
12. J. R. Vergara and P. A. Estévez, "A review of feature selection methods based on mutual information," Neural Comput. Appl., vol. 24, no. 1, pp. 175-186, 2014.
13. Y. Zhai, Y.-S. Ong, and I. W. Tsang, "The emerging 'big dimensionality,"' IEEE Comput. Intell. Mag., vol. 9, no. 3, pp. 14-26, Aug. 2014.
14. B. de la Iglesia, "Evolutionary computation for feature selection in classification problems," Wiley Interdiscipl. Rev. Data Min. Knowl. Disc., vol. 3, no. 6, pp. 381-407, 2013.
15. S. Ahmed, M. Zhang, and L. Peng, "Enhanced feature selection for biomarker discovery in LC-MS data using GP," in Proc. IEEE Congr. Evol. Comput. (CEC), Cancún, Mexico, 2013, pp. 584-591.
16. M. H. Aghdam, N. Ghasem-Aghaee, and M. E. Basiri, "Text feature selection using ant colony optimization," Expert Syst. Appl., vol. 36, no. 3, pp. 6843-6853, 2009.
17. A. Ghosh, A. Datta, and S. Ghosh, "Self-adaptive differential evolution for feature selection in hyperspectral image data," Appl. Soft Comput., vol. 13, no. 4, pp. 1969-1977, 2013.
18. K. Krawiec, "Evolutionary feature selection and construction," in Encyclopedia of Machine Learning. New York, NY, USA: Springer, 2010, pp. 353-357.
19. D. P. Muni, N. R. Pal, and J. Das, "Genetic programming for simultaneous feature selection and classifier design," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 36, no. 1, pp. 106-117, Feb. 2006.
20. L. Jung-Yi, K. Hao-Ren, C. Been-Chian, and Y. Wei-Pang, "Classifier design with feature selection and feature extraction using layered genetic programming," Expert Syst. Appl., vol. 34, no. 2, pp. 1384-1393, 2008.
21. R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artif. Intell., vol. 97, nos. 1-2, pp. 273-324, 1997.
22. M. Mitchell, An Introduction to Genetic Algorithms. Cambridge, MA, USA: MIT Press, 1996.
23. A. W. Whitney, "A direct method of nonparametric measurement selection," IEEE Trans. Comput., vol. C-20, no. 9, pp. 1100-1103, Sep. 1971.
24. T. Marill and D. M. Green, "On the effectiveness of receptors in recognition systems," IEEE Trans. Inf. Theory, vol. 9, no. 1, pp. 11-17, Jan. 1963.
25. S. D. Stearns, "On selecting features for pattern classifier," in Proc. 3rd Int. Conf. Pattern Recognit., Coronado, CA, USA, pp. 71-75, 1976.
26. P. Pudil, J. Novovičová, and J. V. Kittler, "Floating search methods in feature selection," Pattern Recognit. Lett., vol. 15, no. 11, pp. 1119-1125, 1994.
27. Q. Mao and I. W.-H. Tsang, "A feature selection method for multivariate performance measures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 9, pp. 2051-2063, Sep. 2013.
28. F. Min, Q. Hu, and W. Zhu, "Feature selection with test cost constraint," Int. J. Approx. Reason., vol. 55, no. 1, pp. 167-179, 2014.
29. B. Xue, M. Zhang, and W. N. Browne, "Particle swarm optimization for feature selection in classification: A multi-objective approach," IEEE Trans. Cybern., vol. 43, no. 6, pp. 1656-1671, Dec. 2013.
30. K. Kira and L. A. Rendell, "A practical approach to feature selection," in Proc. 9th Int. Workshop Mach. Learn., San Francisco, CA, USA, 1992, pp. 249-256.
31. S. Wang, W. Pedrycz, Q. Zhu, and W. Zhu, "Subspace learning for unsupervised feature selection via matrix factorization," Pattern Recognit., vol. 48, no. 1, pp. 10-19, 2015.
32. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, and minredundancy," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226-1238, Aug. 2005.
33. A. Unler, R. B. Chinnam, and A. Murat, "mr2 PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification," Inf. Sci., vol. 181, no. 20, pp. 4625-4641, 2011.
34. L. Cervante, B. Xue, M. Zhang, and L. Shang, "Binary particle swarm optimisation for feature selection: A filter based approach," in Proc. IEEE Congr. Evol. Comput. (CEC), Brisbane, QLD, Australia, 2012, pp. 1-8.
35. N. C. Tan, W. G. Fisher, K. P. Rosenblatt, and H. R. Garner, "Application of multiple statistical tests to enhance mass spectrometrybased biomarker discovery," BMC Bioinformat., vol. 10, p. 144, May 2009.
36. M. Tan, I. W. Tsang, and L. Wang, "Minimax sparse logistic regression for very high-dimensional feature selection," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 10, pp. 1609-1622, Oct. 2013.
37. W. Siedlecki and J. Sklansky, "A note on genetic algorithms for large-scale feature selection," Pattern Recognit. Lett., vol. 10, no. 5, pp. 335-347, 1989.
38. I. A. Gheyas and L. S. Smith, "Feature subset selection in large dimensionality domains," Pattern Recognit., vol. 43, no. 1, pp. 5-13, 2010.
39. Y.-S. Jeong, K. S. Shin, and M. K. Jeong, "An evolutionary algorithm with the partial sequential forward floating search mutation for largescale feature selection problems," J. Oper. Res. Soc., vol. 66, no. 4, pp. 529-538, 2014.
40. A. Purohit, N. S. Chaudhari, and A. Tiwari, "Construction of classifier with feature selection based on genetic programming," in Proc. IEEE Congr. Evol. Comput. (CEC), Barcelona, Spain, 2010, pp. 1-5.
41. K. Neshatian and M. Zhang, "Improving relevance measures using genetic programming," in Proc. Eur. Conf. Genet. Program. (EuroGP), Málaga, Spain, 2012, vol. 7244, pp. 97-108.
42. D. K. Agrafiotis and W. Cedeño, "Feature selection for structureactivity correlation using binary particle swarms," J. Med. Chem., vol. 45, no. 5, pp. 1098-1107, 2002.
43. Z. Yan and C. Yuan, "Ant colony optimization for feature selection in face recognition," in Biometric Authentication (LNCS 3072). Heidelberg, Germany: Springer, 2004, pp. 221-226.
44. L. E. A. Santana, L. Silva, A. M. P. Canuto, F. Pintro, and K. O. Vale, "A comparative analysis of genetic algorithm and ant colony optimization to select attributes for an heterogeneous ensemble of classifiers," in Proc. IEEE Congr. Evol. Comput., Barcelona, Spain, 2010, pp. 1-8.
45. B. Chen, L. Chen, and Y. Chen, "Efficient ant colony optimization for image feature selection," Signal Process., vol. 93, no. 6, pp. 1566-1576, 2012.
46. X. Zhao et al., "Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton," Appl. Soft Comput., vol. 24, pp. 585-596, Nov. 2014.
47. R. N. Khushaba, A. Al-Ani, A. AlSukker, and A. Al-Jumaily, "A combined ant colony and differential evolution feature selection algorithm," in Ant Colony Optimization and Swarm Intelligence (LNCS 5217). Heidelberg, Germany: Springer, 2008, pp. 1-12.
48. L. Wang et al., "Feature selection based on meta-heuristics for biomedicine," Optim. Methods Softw., vol. 29, no. 4, pp. 703-719, 2014.
49. Z. Zhu, Y.-S. Ong, and M. Dash, "Markov blanket-embedded genetic algorithm for gene selection," Pattern Recognit., vol. 40, no. 11, pp. 3236-3248, 2007.
50. S. S. Kannan and N. Ramaraj, "A novel hybrid feature selection via symmetrical uncertainty ranking based local memetic search algorithm," Knowl. Based Syst., vol. 23, no. 6, pp. 580-585, 2010.
51. M. Iqbal, S. S. Naqvi, W. N. Browne, C. Hollitt, and M. Zhang, "Salient object detection using learning classifier systems that compute action mappings," in Proc. 16th Annu. Conf. Genet. Evol. Comput. (GECCO), Vancouver, BC, Canada, 2014, pp. 525-532.
52. I. M. Alvarez, W. N. Browne, and M. Zhang, "Reusing learned functionality in XCS: Code fragments with constructed functionality and constructed features," in Proc. Conf. Compan. Genet. Evol. Comput. Compan. (GECCO), Vancouver, BC, Canada, 2014, pp. 969-976.
53. I. Vatolkin, W. Theimer, and G. Rudolph, "Design and comparison of different evolution strategies for feature selection and consolidation in music classification," in Proc. IEEE Congr. Evol. Comput., Trondheim, Norway, 2009, pp. 174-181.
54. M. Marinaki and Y. Marinakis, "A bumble bees mating optimization algorithm for the feature selection problem," Int. J. Mach. Learn. Cybern., pp. 1-20, Jun. 2014.
55. E. Hancer, B. Xue, M. Zhang, D. Karaboga, and B. Akay, "A multiobjective artificial bee colony approach to feature selection using fuzzy mutual information," in Proc. IEEE Congr. Evol. Comput. (CEC), Sendai, Japan, May 2015, pp. 2420-2427.
56. S.-W. Lin and S.-C. Chen, "Parameter tuning, feature selection and weight assignment of features for case-based reasoning by artificial immune system," Appl. Soft Comput., vol. 11, no. 8, pp. 5042-5052, 2011.
57. K.-J. Wang, K.-H. Chen, and M.-A. Angelia, "An improved artificial immune recognition system with the opposite sign test for feature selection," Knowl. Based Syst., vol. 71, pp. 126-145, Nov. 2014.
58. M. Komosiński and K. Krawiec, "Evolutionary weighting of image features for diagnosing of CNS tumors," Artif. Intell. Med., vol. 19, no. 1, pp. 25-38, 2000.
59. R. Leardi, R. Boggia, and M. Terrile, "Genetic algorithms as a strategy for feature selection," J. Chemometr., vol. 6, no. 5, pp. 267-281, 1992.
60. H. Vafaie and K. De Jong, "Feature space transformation using genetic algorithms," IEEE Intell. Syst., vol. 13, no. 2, pp. 57-65, Mar./Apr. 1998.
61. J. Yang and V. Honavar, "Feature subset selection using a genetic algorithm," IEEE Intell. Syst. Appl., vol. 13, no. 2, pp. 44-49, Mar./Apr. 1998.
62. M. Demirekler and A. Haydar, "Feature selection using genetics-based algorithm and its application to speaker identification," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., Phoenix, AZ, USA, 1999, vol. 1, pp. 329-332.
63. T. Nakashima and H. Ishibuchi, "Multi-objective pattern and feature selection by a genetic algorithm," in Proc. 2nd Annu. Conf. Genet. Evol. Comput. Conf. (GECCO), Las Vegas, NV, USA, 2000, pp. 1069-1076.
64. M. G. Smith and L. Bull, "Genetic programming with a genetic algorithm for feature construction and selection," Genet. Program. Evol. Mach., vol. 6, no. 3, pp. 265-281, 2005.
65. P. Scheunders, S. Yu, and S. De Backer, "Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery," Pattern Recognit. Lett., vol. 23, nos. 1-3, pp. 183-190, 2002.
66. K. Umamaheswari, S. Sumathi, and S. N. Sivanandam, "Neuro-genetic approaches to classification of face images with effective feature selection using hybrid classifiers," in Proc. Int. Conf. Adv. Comput. Commun., Surathkal, India, 2006, pp. 286-291.
67. M. Zamalloa, G. Bordel, L. J. Rodriguez, and M. Penagarikano, "Feature selection based on genetic algorithms for speaker recognition," in Proc. IEEE Odyssey Speaker Lang. Recognit. Workshop, San Juan, PR, USA, 2006, pp. 1-8.
68. M. A. Shoorehdeli, M. Teshnehlab, and H. A. Moghaddam, "Feature subset selection for face detection using genetic algorithms and particle swarm optimization," in Proc. IEEE Int. Conf. Netw. Sens. Control (ICNSC), Fort Lauderdale, FL, USA, 2006, pp. 686-690.
69. J.-H. Hong and S.-B. Cho, "Efficient huge-scale feature selection with speciated genetic algorithm," Pattern Recognit. Lett., vol. 27, no. 2, pp. 143-150, 2006.
70. E. Alba, J. Garcia-Nieto, L. Jourdan, and E. Talbi, "Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms," in Proc. IEEE Congr. Evol. Comput. (CEC), Singapore, 2007, pp. 284-290.
71. D. Chen, K. C. C. Chan, and X. Wu, "Gene expression analyses using genetic algorithm based hybrid approaches," in Proc. IEEE Congr. Evol. Comput., Hong Kong, 2008, pp. 963-969.
72. F. Tan, X. Z. Fu, Y. Q. Zhang, and A. G. Bourgeois, "A genetic algorithm-based method for feature subset selection," Soft Comput., vol. 12, no. 2, pp. 111-120, 2008.
73. L.-H. Chen and H.-D. Hsiao, "Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study," Expert Syst. Appl., vol. 35, no. 3, pp. 1145-1155, 2008.
74. H.-W. Cho et al., "Genetic algorithm-based feature selection in high-resolution NMR spectra," Expert Syst. Appl., vol. 35, no. 3, pp. 967-975, 2008.
75. Y. Li, S. Zhang, and X. Zeng, "Research of multi-population agent genetic algorithm for feature selection," Expert Syst. Appl., vol. 36, no. 9, pp. 11570-11581, 2009.
76. J. Derrac, S. Garcia, and F. Herrera, "A first study on the use of coevolutionary algorithms for instance and feature selection," in Hybrid Artificial Intelligence Systems (LNCS 5572). Berlin, Germany: Springer, 2009, pp. 557-564.
77. R. Li, J. Lu, Y. Zhang, and T. Zhao, "Dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation," Knowl. Based Syst., vol. 23, no. 3, pp. 195-201, 2010.
78. S. C. Yusta, "Different metaheuristic strategies to solve the feature selection problem," Pattern Recognit. Lett., vol. 30, no. 5, pp. 525-534, 2009.
79. K. Manimala, K. Selvi, and R. Ahila, "Hybrid soft computing techniques for feature selection and parameter optimization in power quality data mining," Appl. Soft Comput., vol. 11, no. 8, pp. 5485-5497, 2011.
80. S. F. Da Silva, M. X. Ribeiro, J. do E. S. Batista Neto, C. Traina, Jr., and A. J. M. Traina, "Improving the ranking quality of medical image retrieval using a genetic feature selection method," Decis. Support Syst., vol. 51, no. 4, pp. 810-820, 2011.
81. S. M. Winkler, M. Affenzeller, W. Jacak, and H. Stekel, "Identification of cancer diagnosis estimation models using evolutionary algorithms: A case study for breast cancer, melanoma, and cancer in the respiratory system," in Proc. 13th Annu. Conf. Compan. Genet. Evol. Comput. (GECCO), Dublin, Ireland, 2011, pp. 503-510.
82. T. C. Chen, Y.-C. Hsieh, P.-S. You, and Y.-C. Lee, "Feature selection and classification by using grid computing based evolutionary approach for the microarray data," in Proc. 3rd IEEE Int. Conf. Comput. Sci. Inf. Technol. (ICCSIT), Chengdu, China, 2010, vol. 9, pp. 85-89.
83. F. Souza, T. Matias, and R. Araujo, "Co-evolutionary genetic multilayer perceptron for feature selection and model design," in Proc. 16th Conf. Emerg. Technol. Factory Autom. (ETFA), Toulouse, France, 2011, pp. 1-7.
84. A. A. Chaaraoui and F. Florez-Revuelta, "Human action recognition optimization based on evolutionary feature subset selection," in Proc. 15th Annu. Conf. Genet. Evol. Comput. (GECCO), Amsterdam, The Netherlands, 2013, pp. 1229-1236.
85. S. Oreski and G. Oreski, "Genetic algorithm-based heuristic for feature selection in credit risk assessment," Expert Syst. Appl., vol. 41, no. 4, pp. 2052-2064, 2014.
86. J.-H. Seo, Y. H. Lee, and Y.-H. Kim, "Feature selection for very shortterm heavy rainfall prediction using evolutionary computation," Adv. Meteorol., vol. 2014, pp. 1-15, Jan. 2014.
87. D. Liang, C.-F. Tsai, and H.-T. Wu, "The effect of feature selection on financial distress prediction," Knowl. Based Syst., vol. 73, pp. 289-297, Jan. 2015.
88. C. Emmanouilidis, A. Hunter, and J. MacIntyre, "A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator," in Proc. IEEE Congr. Evol. Comput., vol. 1. La Jolla, CA, USA, 2000, pp. 309-316.
89. Y. Kim, W. N. Street, and F. Menczer, "Feature selection in unsupervised learning via evolutionary search," in Proc. 6th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., Boston, MA, USA, 2000, pp. 365-369.
90. L. S. Oliveira, R. Sabourin, F. Bortolozzi, and C. Y. Suen, "Feature selection using multi-objective genetic algorithms for handwritten digit recognition," in Proc. 16th Int. Conf. Pattern Recognit. (ICPR), Québec City, QC, Canada, 2002, vol. 1, pp. 568-571.
91. K. Waqas, R. Baig, and S. Ali, "Feature subset selection using multiobjective genetic algorithms," in Proc. IEEE 13th Int. Conf. Multitopic Conf. (INMIC), Islamabad, Pakistan, 2009, pp. 1-6.
92. B. Huang, B. Buckley, and T.-M. Kechadi, "Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications," Expert Syst. Appl., vol. 37, no. 5, pp. 3638-3646, 2010.
93. I. Vatolkin, M. Preuß, G. Rudolph, M. Eichhoff, and C. Weihs, "Multi-objective evolutionary feature selection for instrument recognition in polyphonic audio mixtures," Soft Comput., vol. 16, no. 12, pp. 2027-2047, 2012.
94. A. Mukhopadhyay and U. Maulik, "An SVM-wrapped multiobjective evolutionary feature selection approach for identifying cancermicroRNA markers," IEEE Trans. Nanobiosci., vol. 12, no. 4, pp. 275-281, Dec. 2013.
95. L. D. Vignolo, D. H. Milone, and J. Scharcanskia, "Feature selection for face recognition based on multi-objective evolutionary wrappers," Expert Syst. Appl., vol. 40, no. 13, pp. 5077-5084, 2013.
96. C. J. Tan, C. P. Lim, and Y.-N. Cheah, "A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models," Neurocomputing, vol. 125, pp. 217-228, Feb. 2014.
97. E. de la Hoz, E. de la Hoz, A. Ortiz, J. Ortega, and A. Martínez-Álvarez, "Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps," Knowl. Based Syst., vol. 71, pp. 322-338, Nov. 2014.
98. P. L. Lanzi, "Fast feature selection with genetic algorithms: A filter approach," in Proc. IEEE Int. Conf. Evol. Comput., Indianapolis, IN, USA, 1997, pp. 537-540.
99. B. Chakraborty, "Genetic algorithm with fuzzy fitness function for feature selection," in Proc. IEEE Int. Symp. Ind. Electron. (ISIE), L'Aquila, Italy, 2002, vol. 1, pp. 315-319.
100. W. Sheng, X. Liu, and M. Fairhurst, "A niching memetic algorithm for simultaneous clustering and feature selection," IEEE Trans. Knowl. Data Eng., vol. 20, no. 7, pp. 868-879, Jul. 2008.
101. A. Saxena, N. R. Pal, and M. Vora, "Evolutionary methods for unsupervised feature selection using Sammon's stress function," Fuzzy Inf. Eng., vol. 2, no. 3, pp. 229-247, 2010.
102. B. Xue, L. Cervante, L. Shang, W. N. Browne, and M. Zhang, "Multiobjective evolutionary algorithms for filter based feature selection in classification," Int. J. Artif. Intell. Tools, vol. 22, no. 4, 2013, Art. ID 1350024.
103. M. Banerjee, S. Mitra, and H. Banka, "Evolutionary rough feature selection in gene expression data," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 37, no. 4, pp. 622-632, Jul. 2007.
104. P. P. Kundu and S. Mitra, "Multi-objective evolutionary feature selection," in Pattern Recognition and Machine Intelligence (LNCS 5909). Heidelberg, Germany: Springer, 2009, pp. 74-79.
105. N. Spolaôr, A. C. Lorena, and H. D. Lee, "Multi-objective genetic algorithm evaluation in feature selection," in Evolutionary Multi-Criterion Optimization (LNCS 6576). Heidelberg, Germany: Springer, 2011, pp. 462-476.
106. H. Xia, J. Zhuang, and D. Yu, "Multi-objective unsupervised feature selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis," Neurocomputing, vol. 146, pp. 113-124, Dec. 2014.
107. A. M. P. Canuto and D. S. C. Nascimento, "A genetic-based approach to features selection for ensembles using a hybrid and adaptive fitness function," in Proc. Int. Joint Conf. Neural Netw. (IJCNN), Brisbane, QLD, Australia, 2012, pp. 1-8.
108. D. Mazumdar, S. Mitra, and S. Mitra, "Evolutionary-rough feature selection for face recognition," in Transactions on Rough Sets XII (LNCS 6190). Heidelberg, Germany: Springer, 2010, pp. 117-142.
109. P. Sousa, P. Cortez, R. Vaz, M. Rocha, and M. Rio, "Email spam detection: A symbiotic feature selection approach fostered by evolutionary computation," Int. J. Inf. Technol. Decis. Making, vol. 12, no. 4, pp. 863-884, 2013.
110. D. Chyzhyk, A. Savio, and M. Graña, "Evolutionary ELM wrapper feature selection for Alzheimer's disease CAD on anatomical brain MRI," Neurocomputing, vol. 128, pp. 73-80, Mar. 2014.
111. F. Lin, D. Liang, C.-C. Yeh, and J.-C. Huang, "Novel feature selection methods to financial distress prediction," Expert Syst. Appl., vol. 41, no. 5, pp. 2472-2483, 2014.
112. A. A. Yahya, A. Osman, A. R. Ramli, and A. Balola, "Feature selection for high dimensional data: An evolutionary filter approach," J. Comput. Sci., vol. 7, no. 5, pp. 800-820, 2011.
113. K. Krawiec, "Genetic programming-based construction of features for machine learning and knowledge discovery tasks," Genet. Program. Evol. Mach., vol. 3, no. 4, pp. 329-343, 2002.
114. R. A. Davis, A. J. Charlton, S. Oehlschlager, and J. C. Wilson, "Novel feature selection method for genetic programming using metabolomic 1H NMR data," Chemometr. Intell. Lab. Syst., vol. 81, no. 1, pp. 50-59, 2006.
115. R. Hunt, K. Neshatian, and M. Zhang, "A genetic programming approach to hyper-heuristic feature selection," in Simulated Evolution and Learning (LNCS 7673). Heidelberg, Germany: Springer, 2012, pp. 320-330.
116. I. Sandin et al., "Aggressive and effective feature selection using genetic programming," in Proc. IEEE Congr. Evol. Comput. (CEC), Brisbane, QLD, Australia, 2012, pp. 1-8.
117. S. Ahmed, M. Zhang, and L. Peng, "Genetic programming for biomarker detection in mass spectrometry data," in Advances in Artificial Intelligence (LNCS 7691). Heidelberg, Germany: Springer, 2012, pp. 266-278.
118. S. Ahmed, M. Zhang, and L. Peng, "Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming," Connect. Sci., vol. 26, no. 3, pp. 215-243, 2014.
119. S. Ahmed, M. Zhang, and L. Peng, "Feature selection and classification of high dimensional mass spectrometry data: A genetic programming approach," in Proc. EvoBio, Vienna, Austria, 2013, vol. 7833, pp. 43-55.
120. K. Neshatian and M. Zhang, "Using genetic programming for contextsensitive feature scoring in classification problems," Connect. Sci., vol. 23, no. 3, pp. 183-207, 2011.
121. A. Friedlander, K. Neshatian, and M. Zhang, "Meta-learning and feature ranking using genetic programming for classification: Variable terminal weighting," in Proc. IEEE Congr. Evol. Comput., New Orleans, LA, USA, 2011, pp. 941-948.
122. S. Ahmed, M. Zhang, and L. Peng, "Prediction of detectable peptides in ms data using genetic programming," in Proc. Compan. Conf. Genet. Evol. Comput. Compan. (GECCO), Vancouver, BC, Canada, 2014, pp. 37-38.
123. J. Sherrah, R. E. Bogner, and A. Bouzerdoum, "Automatic selection of features for classification using genetic programming," in Proc. Aust. New Zealand Conf. Intell. Inf. Syst., Adelaide, SA, Australia, 1996, pp. 284-287.
124. V. Venkatraman, A. R. Dalby, and Z. R. Yang, "Evaluation of mutual information and genetic programming for feature selection in QSAR," J. Chem. Inf. Comput. Sci., vol. 44, no. 5, pp. 1686-1692, 2004.
125. B.-C. Chien and J.-H. Yang, "Features selection based on rough membership and genetic programming," in Proc. IEEE Int. Conf. Syst. Man Cybern. (SMC), Taipei, Taiwan, 2006, vol. 5, pp. 4124-4129.
126. D. Y. Harvey and M. D. Todd, "Automated feature design for numeric sequence classification by genetic programming," IEEE Trans. Evol. Comput., vol. 19, no. 4, pp. 474-489, Aug. 2015.
127. K. Neshatian and M. Zhang, "Unsupervised elimination of redundant features using genetic programming," in Proc. 22nd Aust. Joint Conf. Artif. Intell., Melbourne, VIC, Australia, 2009, vol. 5866, pp. 432-442.
128. K. Neshatian and M. Zhang, "Dimensionality reduction in face detection: A genetic programming approach," in Proc. 24th Int. Conf. Image Vis. Comput. New Zealand (IVCNZ), Wellington, New Zealand, 2009, pp. 391-396.
129. K. Neshatian and M. Zhang, "Pareto front feature selection: Using genetic programming to explore feature space," in Proc. 11th Annu. Conf. Genet. Evol. Comput. (GECCO), Montreal, QC, Canada, 2009, pp. 1027-1034.
130. K. Neshatian and M. Zhang, "Genetic programming for feature subset ranking in binary classification problems," in Proc. Eur. Conf. Genet. Program., Tübingen, Germany, 2009, pp. 121-132.
131. S. Nguyen, M. Zhang, M. Johnston, and K. C. Tan, "Automatic design of scheduling policies for dynamic multi-objective job shop scheduling via cooperative coevolution genetic programming," IEEE Trans. Evol. Comput., vol. 18, no. 2, pp. 193-208, Apr. 2014.
132. H. Al-Sahaf, M. Zhang, and M. Johnston, "Genetic programming for multiclass texture classification using a small number of instances," in Simulated Evolution and Learning (LNCS 8886). Cham, Switzerland: Springer, 2014, pp. 335-346.
133. I. Babaoglu, O. Findik, and E. Ülker, "A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine," Expert Syst. Appl., vol. 37, no. 4, pp. 3177-3183, 2010.
134. C. Zhang and H. Hu, "Using PSO algorithm to evolve an optimum input subset for a SVM in time series forecasting," in Proc. IEEE Int. Conf. Syst. Man Cybern. (SMC), Waikoloa Village, HI, USA, 2005, vol. 4, pp. 3793-3796.
135. E. K. Tang, P. N. Suganthan, and X. Yao, "Feature selection for microarray data using least squares SVM and particle swarm optimization," in Proc. IEEE Symp. Comput. Intell. Bioinformat. Comput. Biol. (CIBCB), La Jolla, CA, USA, 2005, pp. 1-8.
136. K. Iswandy and A. Koenig, "Feature-level fusion by multi-objective binary particle swarm based unbiased feature selection for optimized sensor system design," in Proc. IEEE Int. Conf. Multisensor Fusion Integr. Intell. Syst., Heidelberg, Germany, 2006, pp. 365-370.
137. G. L. F. Azevedo, G. D. C. Cavalcanti, and E. C. B. C. Filho, "An approach to feature selection for keystroke dynamics systems based on PSO and feature weighting," in Proc. IEEE Congr. Evol. Comput. (CEC), Singapore, 2007, pp. 3577-3584.
138. S.-W. Lin, K.-C. Ying, S.-C. Chen, and Z.-J. Lee, "Particle swarm optimization for parameter determination and feature selection of support vector machines," Expert Syst. Appl., vol. 35, no. 4, pp. 1817-1824, 2008.
139. Q. Shen, W.-M. Shi, and W. Kong, "Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data," Comput. Biol. Chem., vol. 32, no. 1, pp. 53-60, 2008.
140. L.-Y. Chuang, H.-W. Chang, C.-J. Tu, and C.-H. Yang, "Improved binary PSO for feature selection using gene expression data," Comput. Biol. Chem., vol. 32, no. 1, pp. 29-38, 2008.
141. C.-L. Huang and J.-F. Dun, "A distributed PSO-SVM hybrid system with feature selection and parameter optimization," Appl. Soft Comput., vol. 8, no. 4, pp. 1381-1391, 2008.
142. A. Mohemmed, M. Zhang, and M. Johnston, "Particle swarm optimization based Adaboost for face detection," in Proc. IEEE Congr. Evol. Comput. (CEC), Trondheim, Norway, 2009, pp. 2494-2501.
143. S.-W. Lin and S.-C. Chen, "PSOLDA: A particle swarm optimization approach for enhancing classification accuracy rate of linear discriminant analysis," Appl. Soft Comput., vol. 9, no. 3, pp. 1008-1015, 2009.
144. M. S. Mohamad, S. Omatu, S. Deris, and M. Yoshioka, "A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data," IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 6, pp. 813-822, Nov. 2011.
145. L.-Y. Chuang, S.-W. Tsai, and C.-H. Yang, "Improved binary particle swarm optimization using catfish effect for feature selection," Expert Syst. Appl., vol. 38, no. 10, pp. 12699-12707, 2011.
146. S. M. Vieira, L. F. Mendonca, G. J. Farinha, and J. M. C. Sousa, "Metaheuristics for feature selection: Application to sepsis outcome prediction," in Proc. IEEE Congr. Evol. Comput. (CEC), Brisbane, QLD, Australia, 2012, pp. 1-8.
147. B. Xue, M. Zhang, and W. N. Browne, "New fitness functions in binary particle swarm optimisation for feature selection," in Proc. IEEE Congr. Evol. Comput. (CEC), Brisbane, QLD, Australia, 2012, pp. 1-8.
148. B. Xue, M. Zhang, and W. N. Browne, "Single feature ranking and binary particle swarm optimisation based feature subset ranking for feature selection," in Proc. Aust. Comput. Sci. Conf. (ACSC), vol. 122. Melbourne, VIC, Australia, 2012, pp. 27-36.
149. C. Jin, S.-W. Jin, and L.-N. Qin, "Attribute selection method based on a hybrid BPNN and PSO algorithms," Appl. Soft Comput., vol. 12, no. 8, pp. 2147-2155, 2012.
150. B. Xue, M. Zhang, and W. N. Browne, "Novel initialisation and updating mechanisms in PSO for feature selection in classification," in Applications of Evolutionary Computation (LNCS 7835). Heidelberg, Germany: Springer, 2013, pp. 428-438.
151. C. C. O. Ramos, A. N. de Souza, A. X. Falcao, and J. P. Papa, "New insights on nontechnical losses characterization through evolutionary-based feature selection," IEEE Trans. Power Del., vol. 27, no. 1, pp. 140-146, Jan. 2012.
152. S. M. Vieira, L. F. Mendonça, G. J. Farinha, and J. M. C. Sousa, "Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients," Appl. Soft Comput., vol. 13, no. 8, pp. 3494-3504, 2013.
153. M. C. Lane, B. Xue, I. Liu, and M. Zhang, "Particle swarm optimisation and statistical clustering for feature selection," in Advances in Artificial Intelligence (LNCS 8272). Cham, Switzerland: Springer, 2013, pp. 214-220.
154. H. B. Nguyen, B. Xue, I. Liu, and M. Zhang, "PSO and statistical clustering for feature selection: A new representation," in Simulated Evolution and Learning (LNCS 8886). Cham, Switzerland: Springer, 2014, pp. 569-581.
155. B. Tran, B. Xue, and M. Zhang, "Improved PSO for feature selection on high-dimensional datasets," in Simulated Evolution and Learning (LNCS 8886). Cham, Switzerland: Springer, 2014, pp. 503-515.
156. M. C. Lane, B. Xue, I. Liu, and M. Zhang, "Gaussian based particle swarm optimisation and statistical clustering for feature selection," in Evolutionary Computation in Combinatorial Optimisation (LNCS 8600). Berlin, Germany: Springer, 2014, pp. 133-144.
157. B. Xue, M. Zhang, and W. N. Browne, "Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms," Appl. Soft Comput., vol. 18, pp. 261-276, May 2014.
158. H. B. Nguyen, B. Xue, I. Liu, P. Andreae, and M. Zhang, "Gaussian transformation based representation in particle swarm optimisation for feature selection," in Applications of Evolutionary Computation (LNCS 9028). Cham, Switzerland: Springer, 2015, pp. 541-553.
159. Y. Zhang, D. Gong, Y. Hu, and W. Zhang, "Feature selection algorithm based on bare bones particle swarm optimization," Neurocomputing, vol. 148, pp. 150-157, Jan. 2015.
160. B. Xue, M. Zhang, and W. N. Browne, "Multi-objective particle swarm optimisation (PSO) for feature selection," in Proc. 14th Annu. Conf. Genet. Evol. Comput. Conf. (GECCO), Philadelphia, PA, USA, 2012, pp. 81-88.
161. Y. Zhang, C. Xia, D. Gong, and X. Sun, "Multi-objective PSO algorithm for feature selection problems with unreliable data," in Advances in Swarm Intelligence (LNCS 8794). Cham, Switzerland: Springer, 2014, pp. 386-393.
162. X. Wang, J. Yang, X. Teng, W. Xia, and R. Jensen, "Feature selection based on rough sets and particle swarm optimization," Pattern Recognit. Lett., vol. 28, no. 4, pp. 459-471, 2007.
163. B. Chakraborty, "Feature subset selection by particle swarm optimization with fuzzy fitness function," in Proc. 3rd Int. Conf. Intell. Syst. Knowl. Eng. (ISKE), Xiamen, China, 2008, vol. 1, pp. 1038-1042.
164. M. Aneesh, A. A. Masand, and K. Manikantan, "Optimal feature selection based on image pre-processing using accelerated binary particle swarm optimization for enhanced face recognition," Procedia Eng., vol. 30, no. 5, pp. 750-758, 2012.
165. B. Chakraborty and G. Chakraborty, "Fuzzy consistency measure with particle swarm optimization for feature selection," in Proc. IEEE Int. Conf. Syst. Man Cybern. (SMC), Manchester, U.K., 2013, pp. 4311-4315.
166. L. Cervante, B. Xue, L. Shang, and M. Zhang, "Binary particle swarm optimisation and rough set theory for dimension reduction in classification," in Proc. IEEE Congr. Evol. Comput. (CEC), Cancún, Mexico, 2013, pp. 2428-2435.
167. C. Bae, W.-C. Yeh, Y. Y. Chung, and S.-L. Liu, "Feature selection with intelligent dynamic swarm and rough set," Expert Syst. Appl., vol. 37, no. 10, pp. 7026-7032, 2010.
168. S. S. S. Ahmad and W. Pedrycz, "Feature and instance selection via cooperative PSO," in Proc. IEEE Int. Conf. Syst. Man Cybern. (SMC), Anchorage, AK, USA, 2011, pp. 2127-2132.
169. 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 Proc. 25th Aust. Joint Conf. Artif. Intell., Sydney, NSW, Australia, 2012, vol. 7691, pp. 313-325.
170. M. Mandal and A. Mukhopadhyay, "A graph-theoretic approach for identifying non-redundant and relevant gene markers from microarray data using multiobjective binary PSO," PLoS ONE, vol. 9, no. 3, pp. 1-13, 2014, Art. ID. e90949.
171. L. Cervante, B. Xue, L. Shang, and M. Zhang, "A multi-objective feature selection approach based on binary PSO and rough set theory," in Proc. 13th Eur. Conf. Evol. Comput. Comb. Optim. (EvoCOP), Vienna, Austria, 2013, vol. 7832, pp. 25-36.
172. B. Xue, L. Cervante, L. Shang, W. N. Browne, and M. Zhang, "A multi-objective particle swarm optimisation for filter based feature selection in classification problems," Connect. Sci., vol. 24, nos. 2-3, pp. 91-116, 2012.
173. B. Xue, L. Cervante, L. Shang, W. N. Browne, and M. Zhang, "Binary PSO and rough set theory for feature selection: A multi-objective filter based approach," Int. J. Comput. Intell. Appl., vol. 13, no. 2, 2014, Art. ID 1450009.
174. M. A. Esseghir, G. Goncalves, and Y. Slimani, "Adaptive particle swarm optimizer for feature selection," in Proc. Int. Conf. Intell. Data Eng. Autom. Learn. (IDEAL), Paisley, U.K., 2010, pp. 226-233.
175. B. Sahu and D. Mishra, "A novel feature selection algorithm using particle swarm optimization for cancer microarray data," Procedia Eng., vol. 38, pp. 27-31, Sep. 2012.
176. H. B. Nguyen, B. Xue, I. Liu, and M. Zhang, "Filter based backward elimination in wrapper based PSO for feature selection in classification," in Proc. IEEE Congr. Evol. Comput. (CEC), Beijing, China, 2014, pp. 3111-3118.
177. R. Huang and M. He, "Feature selection using double parallel feedforward neural networks and particle swarm optimization," in Proc. IEEE Congr. Evol. Comput. (CEC), Singapore, 2007, pp. 692-696.
178. A. Boubezoul and S. Paris, "Application of global optimization methods to model and feature selection," Pattern Recognit., vol. 45, no. 10, pp. 3676-3686, 2012.
179. R. Fdhila, T. M. Hamdani, and A. M. Alimi, "Distributed MOPSO with a new population subdivision technique for the feature selection," in Proc. Int. Symp. Comput. Intell. Intell. Informat. (ISCIII), Floriana, Malta, 2011, pp. 81-86.
180. R. M. Ramadan and R. F. Abdel-Kader, "Face recognition using particle swarm optimization-based selected features," Int. J. Signal Process. Image Process. Pattern Recognit., vol. 2, no. 2, pp. 51-65, 2009.
181. J. Kennedy and R. C. Eberhart, "A discrete binary version of the particle swarm algorithm," in Proc. IEEE Int. Conf. Syst. Man Cybern., vol. 5. Orlando, FL, USA, 1997, pp. 4104-4108.
182. R. Jensen and Q. Shen, "Finding rough set reducts with ant colony optimization," in Proc. U.K. Workshop Comput. Intell., Bristol, U.K., 2003, pp. 15-22.
183. H.-H. Gao, H.-H. Yang, and X.-Y. Wang, "Ant colony optimization based network intrusion feature selection and detection," in Proc. Int. Conf. Mach. Learn. Cybern., Guangzhou, China, 2005, vol. 6, pp. 3871-3875.
184. R. K. Sivagaminathan and S. Ramakrishnan, "A hybrid approach for feature subset selection using neural networks and ant colony optimization," Expert Syst. Appl., vol. 33, no. 1, pp. 49-60, 2007.
185. S. M. Vieira, J. M. C. Sousa, and T. A. Runkler, "Ant colony optimization applied to feature selection in fuzzy classifiers," in Proc. Found. Fuzzy Logic Soft Comput., Cancún, Mexico, 2007, vol. 4529, pp. 778-788.
186. H. R. Kanan and K. Faez, "An improved feature selection method based on ant colony optimization (ACO) evaluated on face recognition system," Appl. Math. Comput., vol. 205, no. 2, pp. 716-725, 2008.
187. N. M. O'Boyle, D. S. Palmer, F. Nigsch, and J. B. Mitchell, "Simultaneous feature selection and parameter optimisation using an artificial ant colony: Case study of melting point prediction," Chem. Central J., vol. 2, pp. 1-15, Oct. 2008.
188. H. Yu, G. Gu, H. Liu, J. Shen, and J. Zhao, "A modified ant colony optimization algorithm for tumor marker gene selection," Genomics, Proteomics Bioinformat., vol. 7, no. 4, pp. 200-208, 2009.
189. S. M. Vieira, J. M. C. Sousa, and T. A. Runkler, "Two cooperative ant colonies for feature selection using fuzzy models," Expert Syst. Appl., vol. 37, no. 4, pp. 2714-2723, 2010.
190. X. Li, W. Zhu, B. Ji, B. Liu, and C. Ma, "Shape feature selection and weed recognition based on image processing and ant colony optimization," Trans. Chin. Soc. Agr. Eng., vol. 26, no. 10, pp. 178-182, 2010.
191. S. Kashef and H. Nezamabadi-Pour, "A new feature selection algorithm based on binary ant colony optimization," in Proc. 5th Conf. Inf. Knowl. Technol. (IKT), Shiraz, Iran, 2013, pp. 50-54.
192. S. Kashef and H. Nezamabadi-Pour, "An advanced ACO algorithm for feature subset selection," Neurocomputing, vol. 147, pp. 271-279, Jan. 2014.
193. S. M. Vieira, J. M. C. Sousa, and T. A. Runkler, "Multicriteria ant feature selection using fuzzy classifiers," in Swarm Intelligence for Multi-objective Problems in Data Mining (Studies in Computational Intelligence), vol. 242. Heidelberg, Germany: Springer, 2009, pp. 19-36.
194. C.-K. Zhang and H. Hu, "Feature selection using the hybrid of ant colony optimization and mutual information for the forecaster," in Proc. Int. Conf. Mach. Learn. Cybern., Guangzhou, China, 2005, vol. 3, pp. 1728-1732.
195. R. Jensen, "Performing feature selection with ACO," in Swarm Intelligence in Data Mining (Studies in Computational Intelligence), vol. 34. Heidelberg, Germany: Springer, 2006, pp. 45-73.
196. L. Ke, Z. Feng, and Z. Ren, "An efficient ant colony optimization approach to attribute reduction in rough set theory," Pattern Recognit. Lett., vol. 29, no. 9, pp. 1351-1357, 2008.
197. M. He, "Feature selection based on ant colony optimization and rough set theory," in Proc. Int. Symp. Comput. Sci. Comput. Technol., Shanghai, China, 2008, vol. 1, pp. 247-250.
198. S. Zhou, J.-P. Zhang, and B.-K. Su, "Feature selection and classification based on ant colony algorithm for hyperspectral remote sensing images," in Proc. 2nd Int. Congr. Image Signal Process., Tianjin, China, 2009, pp. 1-4.
199. Y. Chen, D. Miao, and R. Wang, "A rough set approach to feature selection based on ant colony optimization," Pattern Recognit. Lett., vol. 31, no. 3, pp. 226-233, 2010.
200. H. Huang, H.-B. Xie, J.-Y. Guo, and H.-J. Chen, "Ant colony optimization-based feature selection method for surface electromyography signals classification," Comput. Biol. Med., vol. 42, no. 1, pp. 30-38, 2012.
201. S. I. Ali and W. Shahzad, "A feature subset selection method based on symmetric uncertainty and ant colony optimization," in Proc. Int. Conf. Emerg. Technol. (ICET), Islamabad, Pakistan, 2012, pp. 1-6.
202. A. J. Tallón-Ballesteros and J. C. Riquelme, "Tackling ant colony optimization meta-heuristic as search method in feature subset selection based on correlation or consistency measures," in Intelligent Data Engineering and Automated Learning (IDEAL) (LNCS 8669). Cham, Switzerland: Springer, 2014, pp. 386-393.
203. L. Ke, Z. Feng, Z. Xu, K. Shang, and Y. Wang, "A multiobjective ACO algorithm for rough feature selection," in Proc. 2nd Pac. Asia Conf. Circuits Commun. Syst. (PACCS), Beijing, China, 2010, vol. 1, pp. 207-210.
204. M. M. Kabir, M. Shahjahan, and K. Murase, "A new hybrid ant colony optimization algorithm for feature selection," Expert Syst. Appl., vol. 39, no. 3, pp. 3747-3763, 2012.
205. M. He, "A rough set based hybrid method to feature selection," in Proc. Int. Symp. Knowl. Acquisit. Model. (KAM), Wuhan, China, 2008, pp. 585-588.
206. B. Xue, W. Fu, and M. Zhang, "Multi-objective feature selection in classification: A differential evolution approach," in Simulated Evolution and Learning (LNCS 8886). Cham, Switzerland: Springer, 2014, pp. 516-528.
207. C.-M. Wang and Y.-F. Huang, "Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data," Expert Syst. Appl., vol. 36, no. 3, pp. 5900-5908, 2009.
208. X. Liu, C. Yu, and Z. Cai, "Differential evolution based band selection in hyperspectral data classification," in Advances in Computation and Intelligence (LNCS 6382). Heidelberg, Germany: Springer, 2010, pp. 86-94.
209. R. N. Khushaba, A. Al-Ani, and A. Al-Jumaily, "Feature subset selection using differential evolution and a statistical repair mechanism," Expert Syst. Appl., vol. 38, no. 9, pp. 11515-11526, 2011.
210. M. N. Omidvar, X. Li, Y. Mei, and X. Yao, "Cooperative co-evolution with differential grouping for large scale optimization," IEEE Trans. Evol. Comput., vol. 18, no. 3, pp. 378-393, Jun. 2014.
211. Z. Yang, K. Tang, and X. Yao, "Scalability of generalized adaptive differential evolution for large-scale continuous optimization," Soft Comput., vol. 15, no. 11, pp. 2141-2155, 2011.
212. K. Chakravarty, D. Das, A. Sinha, and A. Konar, "Feature selection by differential evolution algorithm-A case study in personnel identification," in Proc. IEEE Congr. Evol. Comput. (CEC), Cancún, Mexico, 2013, pp. 892-899.
213. C.-S. Yang, L.-Y. Chuang, Y.-J. Chen, and C.-H. Yang, "Feature selection using memetic algorithms," in Proc. 3rd Int. Conf. Conver. Hybrid Inf. Technol., Busan, Korea, 2008, vol. 1, pp. 416-423.
214. M. Abedini, M. Kirley, and R. Chiong, "Incorporating feature ranking and evolutionary methods for the classification of high-dimensional DNA microarray gene expression data," Aust. Med. J., vol. 6, no. 5, pp. 272-279, 2013.
215. A. Al-Ani, A. Alsukker, and R. N. Khushaba, "Feature subset selection using differential evolution and a wheel based search strategy," Swarm Evol. Comput., vol. 9, pp. 15-26, Apr. 2013.
216. P. T. Bharathi and P. Subashini, "Optimal feature subset selection using differential evolution and extreme learning machine," Int. J. Sci. Res., vol. 3, no. 7, pp. 1898-1905, 2014.
217. Z. Li, Z. Shang, B. Y. Qu, and J. J. Liang, "Feature selection based on manifold-learning with dynamic constraint handling differential evolution," in Proc. IEEE Congr. Evol. Comput., Beijing, China, 2014, pp. 332-337.
218. I.-S. Oh, J.-S. Lee, and B.-R. Moon, "Hybrid genetic algorithms for feature selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 11, pp. 1424-1437, Nov. 2004.
219. O. Alomari and Z. A. Othman, "Bees algorithm for feature selection in network anomaly detection," J. Appl. Sci. Res., vol. 8, no. 3, pp. 1748-1756, 2012.
220. S. Palanisamy and S. Kanmani, "Artificial bee colony approach for optimizing feature selection," Int. J. Comput. Sci. Issues, vol. 9, no. 3, pp. 432-438, 2012.
221. V. Chahkandi, M. Yaghoobi, and G. Veisi, "Feature selection with chaotic hybrid artificial bee colony algorithm based on fuzzy (CHABCF)," J. Soft Comput. Appl., vol. 2013, pp. 1-8, Dec. 2013.
222. E. Hancer, B. Xue, D. Karaboga, and M. Zhang, "A binary ABC algorithm based on advanced similarity scheme for feature selection," Appl. Soft Comput., vol. 36, pp. 334-348, Nov. 2015.
223. K. Shelke, S. Jayaraman, S. Ghosh, and J. Valadi, "Hybrid feature selection and peptide binding affinity prediction using an EDA based algorithm," in Proc. IEEE Congr. Evol. Comput. (CEC), Cancún, Mexico, 2013, pp. 2384-2389.
224. X. Han et al., "Feature subset selection by gravitational search algorithm optimization," Inf. Sci., vol. 281, pp. 128-146, Oct. 2014.
225. H. Zhang and G. Sun, "Feature selection using tabu search method," Pattern Recognit., vol. 35, no. 3, pp. 701-711, Mar. 2002.
226. Z. Zhu, S. Jia, and Z. Ji, "Towards a memetic feature selection paradigm [application notes]," IEEE Comput. Intell. Mag., vol. 5, no. 2, pp. 41-53, May 2010.
227. Z. Zhu and Y.-S. Ong, "Memetic algorithms for feature selection on microarray data," in Advances in Neural Networks (ISNN) (LNCS 4491). Heidelberg, Germany: Springer, 2007, pp. 1327-1335.
228. J. Huang, Y. Cai, and X. Xu, "A hybrid genetic algorithm for feature selection wrapper based on mutual information," Pattern Recognit. Lett., vol. 28, no. 13, pp. 1825-1844, 2007.
229. M. A. Esseghir, G. Goncalves, and Y. Slimani, "Memetic feature selection: Benchmarking hybridization schemata," in Hybrid Artificial Intelligence Systems (LNCS 6076). Heidelberg, Germany: Springer, 2010, pp. 351-358.
230. Y. Wen and H. Xu, "A cooperative coevolution-based pittsburgh learning classifier system embedded with memetic feature selection," in Proc. IEEE Congr. Evol. Comput., New Orleans, LA, USA, 2011, pp. 2415-2422.
231. M. Schiezaro and H. Pedrini, "Data feature selection based on artificial bee colony algorithm," EURASIP J. Image Video Process., vol. 2013, no. 1, pp. 1-8, Dec. 2013.
232. J. Jelonek, K. Krawiec, and R. Slowiński, "Rough set reduction of attributes and their domains for neural networks," Comput. Intell., vol. 11, no. 2, pp. 339-347, 1995.
233. B. Xue, "Particle swarm optimisation for feature selection," Ph.D. dissertation, School Eng. Comput. Sci., Victoria Univ. Wellington, Wellington, New Zealand, 2014.
234. D. Kumar, D. Kumar, and C. S. Rai, "Memetic algorithms for feature selection in face recognition," in Proc. Int. Conf. Hybrid Intell. Syst., Barcelona, Spain, 2008, pp. 931-934.
235. E. Alpaydin, Introduction to Machine Learning. Cambridge, MA, USA: MIT Press, 2004.
236. W. Dong, T. Chen, P. Tino, and X. Yao, "Scaling up estimation of distribution algorithms for continuous optimization," IEEE Trans. Evol. Comput., vol. 17, no. 6, pp. 797-822, Dec. 2013.
237. L. Yu and H. Liu, "Efficient feature selection via analysis of relevance and redundancy," J. Mach. Learn. Res., vol. 5, pp. 1205-1224, Dec. 2004.
238. E. Amaldi and V. Kann, "On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems," Theor. Comput. Sci., vol. 209, nos. 1-2, pp. 237-260, 1998.
239. M. N. Le, Y. S. Ong, S. Menzel, Y. Jin, and B. Sendhoff, "Evolution by adapting surrogates," Evol. Comput., vol. 21, no. 2, pp. 313-340, 2013.
240. P. A. Estévez, M. Tesmer, C. A. Perez, and J. M. Zurada, "Normalized mutual information feature selection," IEEE Trans. Neural Netw., vol. 20, no. 2, pp. 189-201, Feb. 2009.
241. A. Jakulin and I. Bratko, "Testing the significance of attribute interactions," in Proc. 21 Int. Conf. Mach. Learn. (ICML), Banff, AB, Canada, 2004, pp. 52-59.
242. Y. Zhai, M. Tan, I. Tsang, and Y. S. Ong, "Discovering support and affiliated features from very high dimensions," in Proc. 29th Int. Conf. Mach. Learn. (ICML), Edinburgh, U.K., 2012, p. 8.
243. C. A. C. Coello, "Evolutionary multi-objective optimization: A historical view of the field," IEEE Comput. Intell. Mag., vol. 1, no. 1, pp. 28-36, Feb. 2006.
244. V. Khare, X. Yao, and K. Deb, "Performance scaling of multiobjective evolutionary algorithms," in Evolutionary Multi-Criterion Optimization. Berlin, Germany: Springer, 2003, pp. 376-390.
245. K. Praditwong and X. Yao, "How well do multi-objective evolutionary algorithms scale to large problems," in Proc. IEEE Congr. Evol. Comput. (CEC), Singapore, 2007, pp. 3959-3966.
246. K. Neshatian, "Feature manipulation with genetic programming," Ph.D. dissertation, Dept. Comput. Sci., Victoria Univ. Wellington, Wellington, New Zealand, 2010.
247. C. Ambroise and G. J. McLachlan, "Selection bias in gene extraction on the basis of microarray gene-expression data," Proc. Nat. Acad. Sci., vol. 99, no. 10, pp. 6562-6566, 2002.
248. S. K. Singhi and H. Liu, "Feature subset selection bias for classification learning," in Proc. 23rd Int. Conf. Mach. Learn. (ICML), Pittsburgh, PA, USA, 2006, pp. 849-856.
249. U. M. Braga-Neto and E. R. Dougherty, "Is cross-validation valid for small-sample microarray classification?" Bioinformatics, vol. 20, no. 3, pp. 374-380, 2004.
250. J. Tang and H. Liu, "Coselect: Feature selection with instance selection for social media data," in Proc. SIAM Int. Conf. Data Min. (SDM), Austin, TX, USA, 2013, pp. 695-703.