A Review of Feature Selection and Its Methods

1. Yu, L., H. Liu. Efficient Feature Selection via Analysis of Relevance and Redundancy. – J. Mach. Learn. Res., Vol. 5, 2004, No Oct, pp. 1205-1224.Search in Google Scholar

2. Gheyas, I. A., L. S. Smith. Feature Subset Selection in Large Dimensionality Domains. – Pattern Recognit., Vol. 43, January 2010, No 1, pp. 5-13.10.1016/j.patcog.2009.06.009Search in Google Scholar

3. Yang, Y., J. O. Pedersen. A Comparative Study on Feature Selection in Text Categorization. – In: Proc. of 14th International Conference on Machine Learning, ICML’97, 1997, pp. 412-420.Search in Google Scholar

4. Yan, K., D. Zhang. Feature Selection and Analysis on Correlated Gas Sensor Data with Recursive Feature Elimination. – Sensors Actuators, B Chem., Vol. 212, Jun 2015, pp. 353-363.10.1016/j.snb.2015.02.025Search in Google Scholar

5. Jain, A., D. Zongker. Feature Selection: Evaluation, Application, and Small Sample Performance. – IEEE Trans. Pattern Anal. Mach. Intell., Vol. 19, 1997, No 2, pp. 153-158.10.1109/34.574797Search in Google Scholar

6. Gutkin, M., R. Shamir, G. Dror. SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification. – PLoS One, Vol. 4, July 2009, No 7, p. e6416.10.1371/journal.pone.0006416Search in Google Scholar

7. Ang, J. C., A. Mirzal, H. Haron, H. N. A. Hamed. Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection. – IEEE/ACM Trans. Comput. Biol. Bioinforma., Vol. 13, September 2016, No 5, pp. 971-989.10.1109/TCBB.2015.2478454Search in Google Scholar

8. Bins, J., B. A. Draper. Feature Selection from Huge Feature Sets. – In: Proc. of IEEE Int. Conf. Comput. Vis., Vol. 2, 2001, pp. 159-165.Search in Google Scholar

9. Ferri, F., P. Pudil. Comparative Study of Techniques for Large-Scale Feature Selection. – Pattern Recognit. Pract. IV, Vol. 1994, 1994, pp. 403-413.10.1016/B978-0-444-81892-8.50040-7Search in Google Scholar

10. Pudil, P., J. Novovičová, J. Kittler. Floating Search Methods in Feature Selection. – Pattern Recognit. Lett., Vol. 15, November 1994, No 11, pp. 1119-1125.10.1016/0167-8655(94)90127-9Search in Google Scholar

11. Doak, J. An Evaluation of Feature Selection Methods and Their Application to Computer Security. CSE-92-18, 1992. 82 p.Search in Google Scholar

12. Skalak, D. B. Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms. – In: Proc. of 11th International Conference on Machine Learning, 1994, pp. 293-301.10.1016/B978-1-55860-335-6.50043-XSearch in Google Scholar

13. Goldberg, D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Boston, MA, 1989. – Read. Addison-Wesley, 1989.Search in Google Scholar

14. Brassard, P., Gilles, Bratley. Fundamentals of Algorithmics. Englewood Cliffs, NJ, Prentice Hall, 1996.Search in Google Scholar

15. Glover, F. Future Paths for Integer Programming and Links to Artificial Intelligence. – Comput. Oper. Res., Vol. 13, January 1986, No 5, pp. 533-549.10.1016/0305-0548(86)90048-1Search in Google Scholar

16. Li, B., L. Wang, W. Song. Ant Colony Optimization for the Traveling Salesman Problem Based on Ants with Memory. – In: Proc. of 4th International Conference on Natural Computation, 2008, pp. 496-501.10.1109/ICNC.2008.354Search in Google Scholar

17. Nozawa, H. A Neural Network Model as a Globally Coupled Map and Applications Based on Chaos. Chaos an Interdiscip. – J. Nonlinear Sci., Vol. 2, July 1992, No 3, pp. 377-386.10.1063/1.16588012779987Search in Google Scholar

18. Luonan, C., K. Aihara. Chaotic Simulated Annealing by a Neural Network Model with Transient Chaos. – Neural Networks, Vol. 8, 1995, No 6, pp. 915-930.10.1016/0893-6080(95)00033-VSearch in Google Scholar

19. Wang, L., S. Li, F. Tian, X. Fu. A Noisy Chaotic Neural Network for Solving Combinatorial Optimization Problems: Stochastic Chaotic Simulated Annealing. – IEEE Trans. Syst. Man, Cybern. Part B Cybern., Vol. 34, 2004, No 5, pp. 2119-2125.10.1109/TSMCB.2004.829778Search in Google Scholar

20. Narendra, P. M., K. Fukunaga. A Branch and Bound Algorithm for Feature Subset Selection. – IEEE Trans. Comput., Vol. C-26, 1977, No 9, pp. 917-922.10.1109/TC.1977.1674939Search in Google Scholar

21. Land, A., A. Doig. An Automatic Method of Solving Discrete Programming Problems. – Econometrika, Vol. 28, 1960, No 3, pp. 497-520.10.2307/1910129Search in Google Scholar

22. Poli, R., J. Kennedy, T. Blackwell. Particle Swarm Optimization. – Swarm Intell., Vol. 1, October 2007, No 1, pp. 33-57.10.1007/s11721-007-0002-0Search in Google Scholar

23. Dash, M., H. Liu. Feature Selection for Classification. – Intell. Data Anal., Vol. 1, January 1997, No 1-4, pp. 131-156.10.1016/S1088-467X(97)00008-5Search in Google Scholar

24. Fayyad, M. U., K. B. Irani. The Attribute Selection Problem in Decision Tree Generation. – Aaai-92, 1992, pp. 104-110.Search in Google Scholar

25. Liu, H., R. Setiono. A Probabilistic Approach to Feature Selection – A Filter Solution. – In: Proc. of 13th International Conference on Machine Learning, 1996, pp. 319-327.Search in Google Scholar

26. Siedlecki, W., J. Sklansky. On Automatic Feature Selection. – Int. J. Pattern Recognit. Artif. Intell., Vol. 02, Jun 1988, No 02, pp. 197-220.10.1142/S0218001488000145Search in Google Scholar

27. Dy, J. G., C. E. Brodley. Feature Subset Selection and Order Identification for Unsupervised Learning. – In: Proc. of 17th Int. Conf. Mach. Learn ICML’00, 2000, pp. 247-254.Search in Google Scholar

28. John, G. H., R. Kohavi, K. Pfleger. Irrelevant Features and the Subset Selection Problem. – In: Machine Learning Proceedings 1994, 1994, pp. 121-129.10.1016/B978-1-55860-335-6.50023-4Search in Google Scholar

29. Caruana, R., D. Freitag. Greedy Attribute Selection. – In: Proc. Elev. Int. Conf. Mach. Learn., Vol. 48, 1994, pp. 28-36.10.1016/B978-1-55860-335-6.50012-XSearch in Google Scholar

30. Asir, D., S. Appavu, E. Jebamalar. Literature Review on Feature Selection Methods for High-Dimensional Data. – Int. J. Comput. Appl., Vol. 136, February 2016, No 1, pp. 9-17.10.5120/ijca2016908317Search in Google Scholar

31. Das, S. Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection. – Engineering, 2001, pp. 74-81.Search in Google Scholar

32. Talavera, L. Feature Selection as a Preprocessing Step for Hierarchical Clustering. – In: Proc. of 25th Int. Conf. Mach. Learn., 1999, pp. 389-397.Search in Google Scholar

33. Biesiada, J., W. Duch. Feature Selection for High-Dimensional Data – A Pearson Redundancy Based Filter. – In Advances in Soft Computing, Vol. 45, Springer, Berlin, Heidelberg, 2007, pp. 242-249.10.1007/978-3-540-75175-5_30Search in Google Scholar

34. Jin, X., A. Xu, R. Bie, P. Guo. Machine Learning Techniques and Chi-Square Feature Selection for Cancer Classification Using SAGE Gene Expression Profiles. – In: Proc. of 2006 International Conference on Data Mining for Biomedical Applications, Springer-Verlag, 2006, pp. 106-115.10.1007/11691730_11Search in Google Scholar

35. Liao, C., S. Li, Z. Luo. Gene Selection Using Wilcoxon Rank Sum Test and Support Vector Machine for Cancer Classification. – Comput. Intell. Secur., Vol. 4456, 2007, pp. 57-66.10.1007/978-3-540-74377-4_7Search in Google Scholar

36. Vinh, L. T., N. D. Thang, Y.-K. Lee. An Improved Maximum Relevance and Minimum Redundancy Feature Selection Algorithm Based on Normalized Mutual Information. – In: Proc. of 10th IEEE/IPSJ International Symposium on Applications and the Internet, 2010, pp. 395-398.10.1109/SAINT.2010.50Search in Google Scholar

37. Estevez, P. A., M. Tesmer, C. A. Perez, J. M. Zurada. Normalized Mutual Information Feature Selection. – IEEE Trans. Neural Networks, Vol. 20, February 2009, No 2, pp. 189-201.10.1109/TNN.2008.200560119150792Search in Google Scholar

38. Peng, H., F. Long, C. Ding. Feature Selection Based on Mutual Information Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. – IEEE Trans. Pattern Anal. Mach. Intell., Vol. 27, August 2005, No 8, pp. 1226-1238.10.1109/TPAMI.2005.15916119262Search in Google Scholar

39. Kwak, N., Chong-Ho Choi. Input Feature Selection by Mutual Information Based on Parzen Window. – IEEE Trans. Pattern Anal. Mach. Intell., Vol. 24, December 2002, No 12, pp. 1667-1671.10.1109/TPAMI.2002.1114861Search in Google Scholar

40. Kira, K., L. Rendell. A Practical Approach to Feature Selection. – In: Proc. of 9th Int’l Workshop on Machine Learning, 1992, pp. 249-256.10.1016/B978-1-55860-247-2.50037-1Search in Google Scholar

41. Aha, D. W., D. Kibler, M. K. Albert. Instance-Based Learning Algorithms. – Mach. Learn., Vol. 6, January 1991, No 1, pp. 37-66.10.1007/BF00153759Search in Google Scholar

42. Kononenko, I. Estimating Attributes: Analysis and Extensions of RELIEF. Berlin, Heidelberg, Springer, 1994, pp. 171-182.10.1007/3-540-57868-4_57Search in Google Scholar

43. Battiti, R. Using Mutual Information for Selecting Features in Supervised Neural Net Learning. – IEEE Trans. Neural Networks, Vol. 5, July 1994, No 4, pp. 537-550.10.1109/72.29822418267827Search in Google Scholar

44. Yang, H. H., J. Moody. Data Visualization and Feature Selection: New Algorithms for Nongaussian Data. – In: In Advances in Neural Information Processing Systems, 1999, pp. 687-693.Search in Google Scholar

45. Meyer, P. E., G. Bontempi. On the Use of Variable Complementarity for Feature Selection in Cancer Classification. – In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3907. LNCS, Springer, Berlin, Heidelberg, 2006, pp. 91-102.10.1007/11732242_9Search in Google Scholar

46. Song, Q., J. Ni, G. Wang. A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data. – IEEE Trans. Knowl. Data Eng., Vol. 25, January 2013, No 1, pp. 1-14.10.1109/TKDE.2011.181Search in Google Scholar

47. Press, W. H., S. A. Teukolsky, W. T. Vetterling, B. P. Flannery. – Numerical Recipes. 2nd Ed. Cambridge, Cambridge University Press, 1989.Search in Google Scholar

48. Kohavi, R., G. H. John. Wrappers for Feature Subset Selection. – Artif. Intell., Vol. 97, December 1997, No 1-2, pp. 273-324.10.1016/S0004-3702(97)00043-XSearch in Google Scholar

49. Korfiatis, V. C., P. A. Asvestas, K. K. Delibasis, G. K. Matsopoulos. A Classification System Based on a New Wrapper Feature Selection Algorithm for the Diagnosis of Primary and Secondary Polycythemia. – Comput. Biol. Med., Vol. 43, December 2013, No 12, pp. 2118-2126.10.1016/j.compbiomed.2013.09.01624290929Search in Google Scholar

50. Chen, G., J. Chen. A Novel Wrapper Method for Feature Selection and Its Applications. – Neurocomputing, Vol. 159, July 2015, No 1, pp. 219-226.10.1016/j.neucom.2015.01.070Search in Google Scholar

51. Panthong, R., A. Srivihok. Wrapper Feature Subset Selection for Dimension Reduction Based on Ensemble Learning Algorithm. – Procedia Comput. Sci., Vol. 72, 2015, pp. 162-169.10.1016/j.procs.2015.12.117Search in Google Scholar

52. Das, S., P. K. Singh, S. Bhowmik, R. Sarkar, M. Nasipuri. A Harmony Search Based Wrapper Feature Selection Method for Holistic Bangla Word Recognition. – Procedia Comput. Sci., Vol. 89, July 2017, pp. 395-403.10.1016/j.procs.2016.06.087Search in Google Scholar

53. Wang, A., N. An, J. Yang, G. Chen, L. Li, G. Alterovitz. Wrapper-Based Gene Selection with Markov Blanket. – Comput. Biol. Med., Vol. 81, 2017, pp. 11-23.10.1016/j.compbiomed.2016.12.00228006702Search in Google Scholar

54. Masood, M. K., Y. C. Soh, C. Jiang. Occupancy Estimation from Environmental Parameters Using Wrapper and Hybrid Feature Selection. – Appl. Soft Comput. J., Vol. 60, November 2017, pp. 482-494.10.1016/j.asoc.2017.07.003Search in Google Scholar

55. Bermejo, S. Ensembles of Wrappers for Automated Feature Selection in Fish Age Classification. – Comput. Electron. Agric., Vol. 134, March 2017, pp. 27-32.10.1016/j.compag.2017.01.007Search in Google Scholar

56. Khammassi, C., S. Krichen. A GA-LR Wrapper Approach for Feature Selection in Network Intrusion Detection. – Comput. Secur., Vol. 70, September 2017, pp. 255-277.10.1016/j.cose.2017.06.005Search in Google Scholar

57. Mohsenzadeh, Y., H. Sheikhzadeh, A. M. Reza, N. Bathaee, M. M. Kalayeh. The Relevance Sample-Feature Machine: A Sparse Bayesian Learning Approach to Joint Feature-Sample Selection. – IEEE Trans. Cybern., Vol. 43, 2013, No 6, pp. 2241-2254.10.1109/TCYB.2013.226073623782842Search in Google Scholar

58. Tipping, M. M. Sparse Bayesian Learning and the Relevance Vector Machine. – J. Mach. Learn. Res., Vol. 1, 2001, pp. 211-245.Search in Google Scholar

59. Mirzaei, A., Y. Mohsenzadeh, H. Sheikhzadeh. Variational Relevant Sample-Feature Machine: A Fully Bayesian Approach for Embedded Feature Selection. – Neurocomputing, Vol. 241, 2017, pp. 181-190.10.1016/j.neucom.2017.02.057Search in Google Scholar

60. Gu, Q., Z. Li, J. Han. Generalized Fisher Score for Feature Selection. February 2012.Search in Google Scholar

61. Song, L., A. Smola, A. Gretton, K. M. Borgwardt, J. Bedo. Supervised Feature Selection via Dependence Estimation. – In: Proc. of 24th International Conference on Machine Learning (ICML’07), 2007, pp. 823-830.10.1145/1273496.1273600Search in Google Scholar

62. Loog, M., R. P. W. Duin, R. Haeb-Umbach. Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria. – IEEE Trans. Pattern Anal. Mach. Intell., Vol. 23, July 2001, No 7, pp. 762-766.10.1109/34.935849Search in Google Scholar

63. Rodgers, J. L., W. A. Nicewander. Thirteen Ways to Look at the Correlation Coefficient. – Am. Stat., Vol. 42, February 1988, No 1, p. 59.10.2307/2685263Search in Google Scholar

64. Nie, F., F. Nie, S. Xiang, Y. Jia, C. Zhang, S. Yan. Trace Ratio Criterion for Feature Selection. – AAAI, 2008, pp. 671-676.Search in Google Scholar

65. Gretton, A., O. Bousquet, A. Smola, B. Schölkopf. Measuring Statistical Dependence with Hilbert-Schmidt Norms. – Springer, 2005, pp. 63-78.10.1007/11564089_7Search in Google Scholar

66. Tutkan, M., M. C. Ganiz, S. Akyokuş. Helmholtz Principle Based Supervised and Unsupervised Feature Selection Methods for Text Mining. – Inf. Process. Manag., Vol. 52, September 2016, No 5, pp. 885-910.10.1016/j.ipm.2016.03.007Search in Google Scholar

67. Balinsky, A., H. Balinsky, S. Simske. On the Helmholtz Principle for Data Mining. – Hewlett-Packard Dev. Company, LP, 2011.Search in Google Scholar

68. Desolneux, A., L. Moisan, J.-M. Morel. From Gestal Theory to Image Analysis: A Probabilistic Approach. 2008.10.1007/978-0-387-74378-3Search in Google Scholar

69. Martín-Smith, P., J. Ortega, J. Asensio-Cubero, J. Q. Gan, A. Ortiz. A Supervised Filter Method for Multi-Objective Feature Selection in EEG Classification Based on Multi-Resolution Analysis for BCI. – Neurocomputing, Vol. 250, August 2017, pp. 45-56.10.1016/j.neucom.2016.09.123Search in Google Scholar

70. Zhu, Y., X. Zhang, R. Hu, G. Wen. Adaptive Structure Learning for Low-Rank Supervised Feature Selection. – Pattern Recognition Letters, North-Holland, 16 August 2017.10.1016/j.patrec.2017.08.018Search in Google Scholar

71. Bishop, C. M. Neural Networks for Pattern Recognition. Clarendon Press, 1995.10.1201/9781420050646.ptb6Search in Google Scholar

72. Mitra, P., C. A. Murthy, S. K. Pal. Unsupervised Feature Selection Using Feature Similarity. – IEEE Trans. Pattern Anal. Mach. Intell., Vol. 24, March 2002, No 3, pp. 301-312.10.1109/34.990133Search in Google Scholar

73. He, X., D. Cai, P. Niyogi. Laplacian Score for Feature Selection. 2006, pp. 507-514.Search in Google Scholar

74. Zhao Z., H. Liu. Spectral Feature Selection for Supervised and Unsupervised Learning. – In: Proc. of 24th International Conference on Machine Learning (ICML’07), 2007, pp. 1151-1157.10.1145/1273496.1273641Search in Google Scholar

75. Cai, D., C. Zhang, X. He. Unsupervised Feature Selection for Multi-Cluster Data. – In: Proc. of 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD’10, 2010, p. 333.10.1145/1835804.1835848Search in Google Scholar

76. Yang, Y., H. T. Shen, Z. Ma, Z. Huang, X. Zhou. l 2,1 - Norm Regularized Discriminative Feature Selection for Unsupervised Learning. – In: Proc. of 22nd Int. Jt. Conf. Artif. Intell., Vol. 2, 2011, pp. 1589-1594.Search in Google Scholar

77. Li, Z., Y. Yang, J. Liu, X. Zhou, H. Lu. Unsupervised Feature Selection Using Nonnegative Spectral Analysis. – In: Proc. of 26th AAAI Conference on Artificial Intelligence, AAAI Press, 2012, pp. 1026-1032.10.1609/aaai.v26i1.8289Search in Google Scholar

78. Bandyopadhyay, S., T. Bhadra, P. Mitra, U. Maulik. Integration of Dense Subgraph Finding with Feature Clustering for Unsupervised Feature Selection. – Pattern Recognit. Lett., Vol. 40, April 2014, No 1, pp. 104-112.10.1016/j.patrec.2013.12.008Search in Google Scholar

79. Wang, X., X. Zhang, Z. Zeng, Q. Wu, J. Zhang. Unsupervised Spectral Feature Selection with l1-Norm Graph. – Neurocomputing, Vol. 200, August 2016, pp. 47-54.10.1016/j.neucom.2016.03.017Search in Google Scholar

80. Nie, F., Z. Zeng, I. W. Tsang, D. Xu, C. Zhang. Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering. – IEEE Trans. Neural Networks, Vol. 22, November 2011, No 11, pp. 1796-1808.10.1109/TNN.2011.216200021965198Search in Google Scholar

81. Wen, J., Z. Lai, Y. Zhan, J. Cui. The L2,1-Norm-Based Unsupervised Optimal Feature Selection with Applications to Action Recognition. – Pattern Recognit., Vol. 60, December 2016, pp. 515-530.10.1016/j.patcog.2016.06.006Search in Google Scholar

82. Wang, S., H. Wang. Unsupervised Feature Selection via Low-Rank Approximation and Structure Learning. – Knowledge-Based Syst., Vol. 124, May 2017, pp. 70-79.10.1016/j.knosys.2017.03.002Search in Google Scholar

83. Liu, Y., K. Liu, C. Zhang, J. Wang, X. Wang. Unsupervised Feature Selection via Diversity-Induced Self-Representation. – Neurocomputing, Vol. 219, January 2017, pp. 350-363.10.1016/j.neucom.2016.09.043Search in Google Scholar

84. Zhu, P., W. Zuo, L. Zhang, Q. Hu, S. C. K. Shiu. Unsupervised Feature Selection by Regularized Self-Representation. – Pattern Recognit., Vol. 48, February 2015, No 2, pp. 438-446.10.1016/j.patcog.2014.08.006Search in Google Scholar

85. Hu, R. et al. Graph Self-Representation Method for Unsupervised Feature Selection. – Neurocomputing, Vol. 220, January 2017, pp. 130-137.10.1016/j.neucom.2016.05.081Search in Google Scholar

86. Du, S., Y. Ma, S. Li, Y. Ma. Robust Unsupervised Feature Selection via Matrix Factorization. – Neurocomputing, Vol. 241, Jun 2017, pp. 115-127.10.1016/j.neucom.2017.02.034Search in Google Scholar

87. Qi, M., T. Wang, F. Liu, B. Zhang, J. Wang, Y. Yi. Unsupervised Feature Selection by Regularized Matrix Factorization. – Neurocomputing, Vol. 273, 17 January 2017, Elsevier, pp. 593-610.10.1016/j.neucom.2017.08.047Search in Google Scholar

88. Zhu, X. Semi-Supervised Learning Literature Survey Contents. Learning, 2006.Search in Google Scholar

89. Belkin, M., P. Niyogi. Towards a Theoretical Foundation for Laplacian-Based Manifold Methods. – J. Comput. Syst. Sci., Vol. 74, December 2008, No 8, pp. 1289-1308.10.1016/j.jcss.2007.08.006Search in Google Scholar

90. Blum, A., S. Chawla. Learning from Labeled and Unlabeled Data Using Graph Mincuts. – In: ICML’01, 2001.Search in Google Scholar

91. Zhu,, X., X. Zhu, Z. Ghahramani, J. Lafferty. Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. – In: ICML’03, 2003, pp. 912-919.Search in Google Scholar

92. Zhou, D., O. Bousquet, T. N. Lal, J. Weston, B. Schölkopf. Learning with Local and Global Consistency. – In: NIPS’03, 2003, pp. 321-328.Search in Google Scholar

93. Wang, J., T. Jebara, S.-F. Chang. Graph Transduction via Alternating Minimization. – In: Proc. of 25th International Conference on Machine Learning (ICML’08), 2008, pp. 1144-1151.10.1145/1390156.1390300Search in Google Scholar

94. Zhao, Z., H. Liu. Semi-Supervised Feature Selection via Spectral Analysis. – In: Proc. of 2007 SIAM International Conference on Data Mining, Philadelphia, PA: Society for Industrial and Applied Mathematics, 2007, pp. 641-646.10.1137/1.9781611972771.75Search in Google Scholar

95. Ma, Z., F. Nie, Y. Yang, J. R. R. Uijlings, N. Sebe, A. G. Hauptmann. Discriminating Joint Feature Analysis for Multimedia Data Understanding. – IEEE Trans. Multimed., Vol. 14, December 2012, No 6, pp. 1662-1672.10.1109/TMM.2012.2199293Search in Google Scholar

96. Yang, Y., F. Wu, F. Nie, H. T. Shen, Y. Zhuang, A. G. Hauptmann. Web and Personal Image Annotation by Mining Label Correlation With Relaxed Visual Graph Embedding. – IEEE Trans. Image Process., Vol. 21, March 2012, No 3, pp. 1339-1351.10.1109/TIP.2011.216926921947528Search in Google Scholar

97. Tang, J., R. Hong, S. Yan, T.-S. Chua, G.-J. Qi, R. Jain. Image Annotation by kNN-Sparse Graph-Based Label Propagation over Noisily Tagged Web Images. – ACM Trans. Intell. Syst. Technol., Vol. 2, February 2011, No 2, pp. 1-15.10.1145/1899412.1899418Search in Google Scholar

98. Jia, S., Y. Xie, L. Shen, L. Deng. Hyperspectral Image Classification Using Fisher Criterion-Based Gabor Cube Selection and Multi-Task Joint Sparse Representation. – In: Proc. of 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS’15), 2015, pp. 1-4.10.1109/WHISPERS.2015.8075364Search in Google Scholar

99. Jia, X., B.-C. Kuo, M. M. Crawford. Feature Mining for Hyperspectral Image Classification. – Proc. IEEE, Vol. 101, March 2013, No 3, pp. 676-697.10.1109/JPROC.2012.2229082Search in Google Scholar

100. Amiri, F., M. Rezaei Yousefi, C. Lucas, A. Shakery, N. Yazdani. Mutual Information-Based Feature Selection for Intrusion Detection Systems. – J. Netw. Comput. Appl., Vol. 34, July 2011, No 4, pp. 1184-1199.10.1016/j.jnca.2011.01.002Search in Google Scholar

101. Chen, Y., Y. Li, X.-Q. Cheng, L. Guo. Survey and Taxonomy of Feature Selection Algorithms in Intrusion Detection System. – Inf. Secur. Cryptol., Vol. 4318, November 2006, pp. 153-167.10.1007/11937807_13Search in Google Scholar

102. Mandal, M., A. Mukhopadhyay. An Improved Minimum Redundancy Maximum Relevance Approach for Feature Selection in Gene Expression Data. – Procedia Technol., Vol. 10, January 2013, pp. 20-27.10.1016/j.protcy.2013.12.332Search in Google Scholar

103. Huerta, E. B., B. Duval, J.-K. Hao. A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data. Berlin, Heidelberg, Springer, 2006, pp. 34-44.10.1007/11732242_4Search in Google Scholar

104. Duval, B., J.-K. Hao, J. C. Hernandez Hernandez. A Memetic Algorithm for Gene Selection and Molecular Classification of Cancer. – In: Proc. of 11th Annual Conference on Genetic and Evolutionary Computation (GECCO’09), 2009, p. 201.10.1145/1569901.1569930Search in Google Scholar

105. Chuang, L.-Y., C.-H. Yang, C.-H. Yang. Tabu Search and Binary Particle Swarm Optimization for Feature Selection Using Microarray Data. – J. Comput. Biol., Vol. 16, December 2009, No 12, pp. 1689-1703.10.1089/cmb.2007.021120047491Search in Google Scholar

106. Jirapech-Umpai, T., S. Aitken. Feature Selection and Classification for Microarray Data Analysis: Evolutionary Methods for Identifying Predictive Genes. – BMC Bioinformatics, Vol. 6, Jun 2005, No 1, p. 148.10.1186/1471-2105-6-148118162515958165Search in Google Scholar

107. Roffo, G., S. Melzi. Feature Selection via Eigenvector Centrality, December 2016. pdfs.semanticscholar.org10.1109/ICCV.2015.478Search in Google Scholar

108. Oh, Il-Seok, Jin-Seon Lee, C. Y. Suen. Analysis of Class Separation and Combination of Class-Dependent Features for Handwriting Recognition. – IEEE Trans. Pattern Anal. Mach. Intell., Vol. 21, 1999, No 10, pp. 1089-1094.10.1109/34.799913Search in Google Scholar

109. Kapetanios, G. Variable Selection Using Non-Standard Optimisation of Information Criteria, – Work. Pap. Queen Hapy University of London, No 533, 2005.Search in Google Scholar

110. Al-Ani, A. Feature Subset Selection Using Ant Colony Optimization. – Int. J. Comput. Intell., Vol. 2, 2005, No 1, pp. 53-58.Search in Google Scholar

111. Shen, L., Z. Zhu, S. Jia, J. Zhu, Y. Sun. Discriminative Gabor Feature Selection for Hyperspectral Image Classification. – IEEE Geosci. Remote Sens. Lett., Vol. 10, January 2013, No 1, pp. 29-33.10.1109/LGRS.2012.2191761Search in Google Scholar

112. Yao, C., Y.-F. Liu, B. Jiang, J. Han, J. Han. LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition. – IEEE Trans. Image Process., Vol. 26, November 2017, No 11, pp. 5257-5269.10.1109/TIP.2017.273320028767370Search in Google Scholar

113. Zhang, Y., S. Wang, P. Phillips, G. Ji. Binary PSO with Mutation Operator for Feature Selection Using Decision Tree Applied to Spam Detection. – Knowledge-Based Syst., Vol. 64, July 2014, pp. 22-31.10.1016/j.knosys.2014.03.015Search in Google Scholar

114. Ambusaidi, M. A., X. He, P. Nanda, Z. Tan. Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm. – IEEE Trans. Comput., Vol. 65, October 2016, No 10, pp. 2986-2998.10.1109/TC.2016.2519914Search in Google Scholar

115. Alonso-Atienza, F., et al. Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines. – IEEE Trans. Biomed. Eng., Vol. 61, 2014, No 3, pp. 832-40.10.1109/TBME.2013.229080024239968Search in Google Scholar

116. Roffo, G., S. Melzi, M. Cristani. Infinite Feature Selection. – In: 2015 IEEE International Conference on Computer Vision (ICCV’15), 2015, pp. 4202-4210.10.1109/ICCV.2015.478Search in Google Scholar

117. Zhang, Y., et al. Detection of Subjects and Brain Regions Related to Alzheimer’s Disease Using 3D MRI Scans Based on Eigenbrain and Machine Learning. – Front. Comput. Neurosci., Vol. 9, Jun 2015, p. 66.10.3389/fncom.2015.00066445135726082713Search in Google Scholar

118. Li, D., Y. Zhou, G. Hu, C. J. Spanos. Optimal Sensor Configuration and Feature Selection for AHU Fault Detection and Diagnosis. – IEEE Trans. Ind. Informatics, Vol. 13, Jun 2017, No 3, pp. 1369-1380.10.1109/TII.2016.2644669Search in Google Scholar