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A Review of Feature Selection and Its Methods Cover
By: B. Venkatesh and  J. Anuradha  
Open Access
|Mar 2019

References

  1. 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.
  2. 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.2009.06.009
  3. 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.
  4. 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.2015.02.025
  5. 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. 153-158.10.1109/34.574797
  6. 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.0006416
  7. 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.2015.2478454
  8. 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.
  9. 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/B978-0-444-81892-8.50040-7
  10. 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)90127-9
  11. 11. Doak, J. An Evaluation of Feature Selection Methods and Their Application to Computer Security. CSE-92-18, 1992. 82 p.
  12. 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-X
  13. 13. Goldberg, D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Boston, MA, 1989. – Read. Addison-Wesley, 1989.
  14. 14. Brassard, P., Gilles, Bratley. Fundamentals of Algorithmics. Englewood Cliffs, NJ, Prentice Hall, 1996.
  15. 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)90048-1
  16. 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.2008.354
  17. 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. 377-386.10.1063/1.16588012779987
  18. 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-V
  19. 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.2004.829778
  20. 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.1977.1674939
  21. 21. Land, A., A. Doig. An Automatic Method of Solving Discrete Programming Problems. – Econometrika, Vol. 28, 1960, No 3, pp. 497-520.10.2307/1910129
  22. 22. Poli, R., J. Kennedy, T. Blackwell. Particle Swarm Optimization. – Swarm Intell., Vol. 1, October 2007, No 1, pp. 33-57.10.1007/s11721-007-0002-0
  23. 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)00008-5
  24. 24. Fayyad, M. U., K. B. Irani. The Attribute Selection Problem in Decision Tree Generation. – Aaai-92, 1992, pp. 104-110.
  25. 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.
  26. 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/S0218001488000145
  27. 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.
  28. 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/B978-1-55860-335-6.50023-4
  29. 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-X
  30. 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/ijca2016908317
  31. 31. Das, S. Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection. – Engineering, 2001, pp. 74-81.
  32. 32. Talavera, L. Feature Selection as a Preprocessing Step for Hierarchical Clustering. – In: Proc. of 25th Int. Conf. Mach. Learn., 1999, pp. 389-397.
  33. 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_30
  34. 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_11
  35. 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_7
  36. 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.2010.50
  37. 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.2008.200560119150792
  38. 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.2005.15916119262
  39. 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.2002.1114861
  40. 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/B978-1-55860-247-2.50037-1
  41. 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/BF00153759
  42. 42. Kononenko, I. Estimating Attributes: Analysis and Extensions of RELIEF. Berlin, Heidelberg, Springer, 1994, pp. 171-182.10.1007/3-540-57868-4_57
  43. 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. 537-550.10.1109/72.29822418267827
  44. 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.
  45. 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_9
  46. 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.2011.181
  47. 47. Press, W. H., S. A. Teukolsky, W. T. Vetterling, B. P. Flannery. – Numerical Recipes. 2nd Ed. Cambridge, Cambridge University Press, 1989.
  48. 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-X
  49. 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.2013.09.01624290929
  50. 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.2015.01.070
  51. 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.2015.12.117
  52. 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.2016.06.087
  53. 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.2016.12.00228006702
  54. 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.2017.07.003
  55. 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.2017.01.007
  56. 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.2017.06.005
  57. 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.2013.226073623782842
  58. 58. Tipping, M. M. Sparse Bayesian Learning and the Relevance Vector Machine. – J. Mach. Learn. Res., Vol. 1, 2001, pp. 211-245.
  59. 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.2017.02.057
  60. 60. Gu, Q., Z. Li, J. Han. Generalized Fisher Score for Feature Selection. February 2012.
  61. 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. 823-830.10.1145/1273496.1273600
  62. 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. 762-766.10.1109/34.935849
  63. 63. Rodgers, J. L., W. A. Nicewander. Thirteen Ways to Look at the Correlation Coefficient. – Am. Stat., Vol. 42, February 1988, No 1, p. 59.10.2307/2685263
  64. 64. Nie, F., F. Nie, S. Xiang, Y. Jia, C. Zhang, S. Yan. Trace Ratio Criterion for Feature Selection. – AAAI, 2008, pp. 671-676.
  65. 65. Gretton, A., O. Bousquet, A. Smola, B. Schölkopf. Measuring Statistical Dependence with Hilbert-Schmidt Norms. – Springer, 2005, pp. 63-78.10.1007/11564089_7
  66. 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.2016.03.007
  67. 67. Balinsky, A., H. Balinsky, S. Simske. On the Helmholtz Principle for Data Mining. – Hewlett-Packard Dev. Company, LP, 2011.
  68. 68. Desolneux, A., L. Moisan, J.-M. Morel. From Gestal Theory to Image Analysis: A Probabilistic Approach. 2008.10.1007/978-0-387-74378-3
  69. 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.2016.09.123
  70. 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.2017.08.018
  71. 71. Bishop, C. M. Neural Networks for Pattern Recognition. Clarendon Press, 1995.10.1201/9781420050646.ptb6
  72. 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. 301-312.10.1109/34.990133
  73. 73. He, X., D. Cai, P. Niyogi. Laplacian Score for Feature Selection. 2006, pp. 507-514.
  74. 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. 1151-1157.10.1145/1273496.1273641
  75. 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. 333.10.1145/1835804.1835848
  76. 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.
  77. 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.v26i1.8289
  78. 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.2013.12.008
  79. 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.2016.03.017
  80. 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.2011.216200021965198
  81. 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.2016.06.006
  82. 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.2017.03.002
  83. 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.2016.09.043
  84. 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.2014.08.006
  85. 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.2016.05.081
  86. 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.2017.02.034
  87. 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.2017.08.047
  88. 88. Zhu, X. Semi-Supervised Learning Literature Survey Contents. Learning, 2006.
  89. 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.2007.08.006
  90. 90. Blum, A., S. Chawla. Learning from Labeled and Unlabeled Data Using Graph Mincuts. – In: ICML’01, 2001.
  91. 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.
  92. 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.
  93. 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. 1144-1151.10.1145/1390156.1390300
  94. 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. 641-646.10.1137/1.9781611972771.75
  95. 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.2012.2199293
  96. 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.2011.216926921947528
  97. 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. 1-15.10.1145/1899412.1899418
  98. 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.2015.8075364
  99. 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.2012.2229082
  100. 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.2011.01.002
  101. 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_13
  102. 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.2013.12.332
  103. 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_4
  104. 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. 201.10.1145/1569901.1569930
  105. 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.2007.021120047491
  106. 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. 148.10.1186/1471-2105-6-148118162515958165
  107. 107. Roffo, G., S. Melzi. Feature Selection via Eigenvector Centrality, December 2016. pdfs.semanticscholar.org10.1109/ICCV.2015.478
  108. 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. 1089-1094.10.1109/34.799913
  109. 109. Kapetanios, G. Variable Selection Using Non-Standard Optimisation of Information Criteria, – Work. Pap. Queen Hapy University of London, No 533, 2005.
  110. 110. Al-Ani, A. Feature Subset Selection Using Ant Colony Optimization. – Int. J. Comput. Intell., Vol. 2, 2005, No 1, pp. 53-58.
  111. 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.2012.2191761
  112. 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.2017.273320028767370
  113. 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.2014.03.015
  114. 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.2016.2519914
  115. 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.2013.229080024239968
  116. 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.2015.478
  117. 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.2015.00066445135726082713
  118. 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.2016.2644669
DOI: https://doi.org/10.2478/cait-2019-0001 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 3 - 26
Submitted on: Feb 2, 2018
Accepted on: Feb 7, 2019
Published on: Mar 29, 2019
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2019 B. Venkatesh, J. Anuradha, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.