A Survey on Feature Selection : in the Perspective of Evolutionary Approaches

Authors(3) :-Saravanan R, Subhasri A, Krithiga S

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of the classification algorithm. However, feature selection is a challenging task to many of the problems mainly to the large search space. There are various methods to solve feature selection problems, where evolutionary computation (EC) techniques have recently added much attention and gave some success. However, the alternative approaches do not have complete guidelines on its strengths and weaknesses which lead to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a broad survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

Authors and Affiliations

Saravanan R
Associate professor, Department of Information Technology, SMVEC,Puducherry, Tamil Nadu, India
Subhasri A
UG student, Department of Information Technology, SMVEC, Puducherry, Tamil Nadu, India
Krithiga S
UG student, Department of Information Technology, SMVEC, Puducherry, Tamil Nadu, India

Classification, Data Mining, Evolutionary Computation, Feature Selection, Machine Learning

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  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. 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.
  4. Y. Liu et al., "An improved particle swarm optimization for feature selection," J. Bionic Eng., vol. 8, no. 2, pp. 191-200, 2011.
  5. H. Liu and Z. Zhao, "Manipulating data and dimension reduction meth-ods: Feature selection," in Encyclopedia of Complexity and Systems Science. Berlin, Germany: Springer, 2009, pp. 5348-5359.
  6. 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.
  7. 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.
  8. Y. Zhai, Y.-S. Ong, and I. W. Tsang, "The emerging ‘big dimen-sionality,"’ IEEE Comput. Intell. Mag., vol. 9, no. 3, pp. 14-26, Aug. 2014.
  9. A. W. Whitney, "A direct method of nonparametric measurement selection," IEEE Trans. Comput., vol. C-20, no. 9, pp. 1100-1103, Sep. 1971.
  10. T. Marill and D. M. Green, "On the effectiveness of receptors in recog-nition systems," IEEE Trans. Inf. Theory, vol. 9, no. 1, pp. 11-17, Jan. 1963.
  11. S. D. Stearns, "On selecting features for pattern classifier," in Proc. 3rd Int. Conf. Pattern Recognit., Coronado, CA, USA, pp. 71-75, 1976.
  12. P. Pudil, J. Novovicová,? and J. V. Kittler, "Floating search meth-ods in feature selection," Pattern Recognit. Lett., vol. 15, no. 11.
  13. Q. Mao and I. W.-H. Tsang, "A feature selection method for multivari-ate performance measures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 9, pp. 2051-2063, Sep. 2013.
  14. H. Peng, F. Long, and 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, no. 8.
  15. W. A. Albukhanajer, J. A. Briffa, and Y. Jin, "Evolutionary multiob-jective image feature extraction in the presence of noise," IEEE Trans. Cybern., vol. 45, no. 9, pp. 1757-1768, Sep. 2015.
  16. N. C. Tan, W. G. Fisher, K. P. Rosenblatt, and H. R. Garner, "Application of multiple statistical tests to enhance mass spectrometry-based biomarker discovery," BMC Bioinformat., vol. 10, p. 144, May 2009.
  17. 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.
  18. N. Spolaôr, A. C. Lorena, and H. D. Lee, "Multi-objective genetic algo-rithm evaluation in feature selection," in Evolutionary Multi-Criterion Optimization (LNCS 6576). Heidelberg, Germany: Springer, 2011, a.462-476.
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  20. B. Xue, L. Cervante, L. Shang, W. N. Browne, and M. Zhang, "Multi-objective evolutionary algorithms for filter based feature selection in classification," Int. J. Artif. Intell. Tools, vol. 22, no. 4, 2013, Art. ID 1350024.
  21. H. Xia, J. Zhuang, and D. Yu, "Multi-objective unsupervised fea-ture selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis," Neurocomputing, vol. 146.
  22. 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.
  23. 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.
  24. 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.
  25. 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 res-piratory system," in Proc. 13th Annu. Conf. Compan. Genet. Evol. Comput. (GECCO), Dublin, Ireland, 2011, pp. 503-510.
  26. 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, 1996,284-287.
  27. K. Neshatian and M. Zhang, "Dimensionality reduction in face detec-tion: A genetic programming approach," in Proc. 24th Int. Conf. Image Vis. Comput. New Zealand (IVCNZ), Wellington, New Zealand, 2009, 391-396.
  28. 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.
  29. 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.
  30. M. C. Lane, B. Xue, I. Liu, and M. Zhang, "Gaussian based par-ticle swarm optimisation and statistical clustering for feature selec-tion," in Evolutionary Computation in Combinatorial Optimisation (LNCS 8600). Berlin, Germany: Springer, 2014, pp. 133-144.
  31. 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.
  32. 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.
  33. 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.
  34. C. A. C. Coello, "Evolutionary multi-objective optimization: A his-torical view of the field," IEEE Comput. Intell. Mag., vol. 1, no. 1,28-36, Feb. 2006.
  35. A. S. U. Kamath, K. De Jong, and A. Shehu, "Effective auto-mated feature construction and selection for classification of biological sequences," PLoS One, vol. 9, no. 7, 2014, Art. ID e99982.

Publication Details

Published in : Volume 4 | Issue 5 | March-April 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 870-877
Manuscript Number : IJSRST1841276
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Saravanan R, Subhasri A, Krithiga S, " A Survey on Feature Selection : in the Perspective of Evolutionary Approaches", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 5, pp.870-877, March-April-2018.
Journal URL : https://ijsrst.com/IJSRST1841276
Citation Detection and Elimination     |      | |
  • I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol. 3, pp. 1157-1182, Mar. 2003.
  • 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.
  • Y. Liu et al., "An improved particle swarm optimization for feature selection," J. Bionic Eng., vol. 8, no. 2, pp. 191-200, 2011.
  • H. Liu and Z. Zhao, "Manipulating data and dimension reduction meth-ods: Feature selection," in Encyclopedia of Complexity and Systems Science. Berlin, Germany: Springer, 2009, pp. 5348-5359.
  • 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.
  • 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.
  • Y. Zhai, Y.-S. Ong, and I. W. Tsang, "The emerging ‘big dimen-sionality,"’ IEEE Comput. Intell. Mag., vol. 9, no. 3, pp. 14-26, Aug. 2014.
  • A. W. Whitney, "A direct method of nonparametric measurement selection," IEEE Trans. Comput., vol. C-20, no. 9, pp. 1100-1103, Sep. 1971.
  • T. Marill and D. M. Green, "On the effectiveness of receptors in recog-nition systems," IEEE Trans. Inf. Theory, vol. 9, no. 1, pp. 11-17, Jan. 1963.
  • S. D. Stearns, "On selecting features for pattern classifier," in Proc. 3rd Int. Conf. Pattern Recognit., Coronado, CA, USA, pp. 71-75, 1976.
  • P. Pudil, J. Novovicová,? and J. V. Kittler, "Floating search meth-ods in feature selection," Pattern Recognit. Lett., vol. 15, no. 11.
  • Q. Mao and I. W.-H. Tsang, "A feature selection method for multivari-ate performance measures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 9, pp. 2051-2063, Sep. 2013.
  • H. Peng, F. Long, and 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, no. 8.
  • W. A. Albukhanajer, J. A. Briffa, and Y. Jin, "Evolutionary multiob-jective image feature extraction in the presence of noise," IEEE Trans. Cybern., vol. 45, no. 9, pp. 1757-1768, Sep. 2015.
  • N. C. Tan, W. G. Fisher, K. P. Rosenblatt, and H. R. Garner, "Application of multiple statistical tests to enhance mass spectrometry-based biomarker discovery," BMC Bioinformat., vol. 10, p. 144, May 2009.
  • 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.
  • N. Spolaôr, A. C. Lorena, and H. D. Lee, "Multi-objective genetic algo-rithm evaluation in feature selection," in Evolutionary Multi-Criterion Optimization (LNCS 6576). Heidelberg, Germany: Springer, 2011, a.462-476.
  • 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.
  • B. Xue, L. Cervante, L. Shang, W. N. Browne, and M. Zhang, "Multi-objective evolutionary algorithms for filter based feature selection in classification," Int. J. Artif. Intell. Tools, vol. 22, no. 4, 2013, Art. ID 1350024.
  • H. Xia, J. Zhuang, and D. Yu, "Multi-objective unsupervised fea-ture selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis," Neurocomputing, vol. 146.
  • 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.
  • 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.
  • 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.
  • 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 res-piratory system," in Proc. 13th Annu. Conf. Compan. Genet. Evol. Comput. (GECCO), Dublin, Ireland, 2011, pp. 503-510.
  • 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, 1996,284-287.
  • K. Neshatian and M. Zhang, "Dimensionality reduction in face detec-tion: A genetic programming approach," in Proc. 24th Int. Conf. Image Vis. Comput. New Zealand (IVCNZ), Wellington, New Zealand, 2009, 391-396.
  • 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.
  • 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.
  • M. C. Lane, B. Xue, I. Liu, and M. Zhang, "Gaussian based par-ticle swarm optimisation and statistical clustering for feature selec-tion," in Evolutionary Computation in Combinatorial Optimisation (LNCS 8600). Berlin, Germany: Springer, 2014, pp. 133-144.
  • 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.
  • 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.
  • 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.
  • C. A. C. Coello, "Evolutionary multi-objective optimization: A his-torical view of the field," IEEE Comput. Intell. Mag., vol. 1, no. 1,28-36, Feb. 2006.
  • A. S. U. Kamath, K. De Jong, and A. Shehu, "Effective auto-mated feature construction and selection for classification of biological sequences," PLoS One, vol. 9, no. 7, 2014, Art. ID e99982.
  • " target="_blank"> BibTeX
    |
  • I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol. 3, pp. 1157-1182, Mar. 2003.
  • 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.
  • Y. Liu et al., "An improved particle swarm optimization for feature selection," J. Bionic Eng., vol. 8, no. 2, pp. 191-200, 2011.
  • H. Liu and Z. Zhao, "Manipulating data and dimension reduction meth-ods: Feature selection," in Encyclopedia of Complexity and Systems Science. Berlin, Germany: Springer, 2009, pp. 5348-5359.
  • 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.
  • 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.
  • Y. Zhai, Y.-S. Ong, and I. W. Tsang, "The emerging ‘big dimen-sionality,"’ IEEE Comput. Intell. Mag., vol. 9, no. 3, pp. 14-26, Aug. 2014.
  • A. W. Whitney, "A direct method of nonparametric measurement selection," IEEE Trans. Comput., vol. C-20, no. 9, pp. 1100-1103, Sep. 1971.
  • T. Marill and D. M. Green, "On the effectiveness of receptors in recog-nition systems," IEEE Trans. Inf. Theory, vol. 9, no. 1, pp. 11-17, Jan. 1963.
  • S. D. Stearns, "On selecting features for pattern classifier," in Proc. 3rd Int. Conf. Pattern Recognit., Coronado, CA, USA, pp. 71-75, 1976.
  • P. Pudil, J. Novovicová,? and J. V. Kittler, "Floating search meth-ods in feature selection," Pattern Recognit. Lett., vol. 15, no. 11.
  • Q. Mao and I. W.-H. Tsang, "A feature selection method for multivari-ate performance measures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 9, pp. 2051-2063, Sep. 2013.
  • H. Peng, F. Long, and 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, no. 8.
  • W. A. Albukhanajer, J. A. Briffa, and Y. Jin, "Evolutionary multiob-jective image feature extraction in the presence of noise," IEEE Trans. Cybern., vol. 45, no. 9, pp. 1757-1768, Sep. 2015.
  • N. C. Tan, W. G. Fisher, K. P. Rosenblatt, and H. R. Garner, "Application of multiple statistical tests to enhance mass spectrometry-based biomarker discovery," BMC Bioinformat., vol. 10, p. 144, May 2009.
  • 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.
  • N. Spolaôr, A. C. Lorena, and H. D. Lee, "Multi-objective genetic algo-rithm evaluation in feature selection," in Evolutionary Multi-Criterion Optimization (LNCS 6576). Heidelberg, Germany: Springer, 2011, a.462-476.
  • 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.
  • B. Xue, L. Cervante, L. Shang, W. N. Browne, and M. Zhang, "Multi-objective evolutionary algorithms for filter based feature selection in classification," Int. J. Artif. Intell. Tools, vol. 22, no. 4, 2013, Art. ID 1350024.
  • H. Xia, J. Zhuang, and D. Yu, "Multi-objective unsupervised fea-ture selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis," Neurocomputing, vol. 146.
  • 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.
  • 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.
  • 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.
  • 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 res-piratory system," in Proc. 13th Annu. Conf. Compan. Genet. Evol. Comput. (GECCO), Dublin, Ireland, 2011, pp. 503-510.
  • 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, 1996,284-287.
  • K. Neshatian and M. Zhang, "Dimensionality reduction in face detec-tion: A genetic programming approach," in Proc. 24th Int. Conf. Image Vis. Comput. New Zealand (IVCNZ), Wellington, New Zealand, 2009, 391-396.
  • 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.
  • 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.
  • M. C. Lane, B. Xue, I. Liu, and M. Zhang, "Gaussian based par-ticle swarm optimisation and statistical clustering for feature selec-tion," in Evolutionary Computation in Combinatorial Optimisation (LNCS 8600). Berlin, Germany: Springer, 2014, pp. 133-144.
  • 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.
  • 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.
  • 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.
  • C. A. C. Coello, "Evolutionary multi-objective optimization: A his-torical view of the field," IEEE Comput. Intell. Mag., vol. 1, no. 1,28-36, Feb. 2006.
  • A. S. U. Kamath, K. De Jong, and A. Shehu, "Effective auto-mated feature construction and selection for classification of biological sequences," PLoS One, vol. 9, no. 7, 2014, Art. ID e99982.
  • " target="_blank">RIS
    |
  • I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol. 3, pp. 1157-1182, Mar. 2003.
  • 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.
  • Y. Liu et al., "An improved particle swarm optimization for feature selection," J. Bionic Eng., vol. 8, no. 2, pp. 191-200, 2011.
  • H. Liu and Z. Zhao, "Manipulating data and dimension reduction meth-ods: Feature selection," in Encyclopedia of Complexity and Systems Science. Berlin, Germany: Springer, 2009, pp. 5348-5359.
  • 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.
  • 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.
  • Y. Zhai, Y.-S. Ong, and I. W. Tsang, "The emerging ‘big dimen-sionality,"’ IEEE Comput. Intell. Mag., vol. 9, no. 3, pp. 14-26, Aug. 2014.
  • A. W. Whitney, "A direct method of nonparametric measurement selection," IEEE Trans. Comput., vol. C-20, no. 9, pp. 1100-1103, Sep. 1971.
  • T. Marill and D. M. Green, "On the effectiveness of receptors in recog-nition systems," IEEE Trans. Inf. Theory, vol. 9, no. 1, pp. 11-17, Jan. 1963.
  • S. D. Stearns, "On selecting features for pattern classifier," in Proc. 3rd Int. Conf. Pattern Recognit., Coronado, CA, USA, pp. 71-75, 1976.
  • P. Pudil, J. Novovicová,? and J. V. Kittler, "Floating search meth-ods in feature selection," Pattern Recognit. Lett., vol. 15, no. 11.
  • Q. Mao and I. W.-H. Tsang, "A feature selection method for multivari-ate performance measures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 9, pp. 2051-2063, Sep. 2013.
  • H. Peng, F. Long, and 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, no. 8.
  • W. A. Albukhanajer, J. A. Briffa, and Y. Jin, "Evolutionary multiob-jective image feature extraction in the presence of noise," IEEE Trans. Cybern., vol. 45, no. 9, pp. 1757-1768, Sep. 2015.
  • N. C. Tan, W. G. Fisher, K. P. Rosenblatt, and H. R. Garner, "Application of multiple statistical tests to enhance mass spectrometry-based biomarker discovery," BMC Bioinformat., vol. 10, p. 144, May 2009.
  • 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.
  • N. Spolaôr, A. C. Lorena, and H. D. Lee, "Multi-objective genetic algo-rithm evaluation in feature selection," in Evolutionary Multi-Criterion Optimization (LNCS 6576). Heidelberg, Germany: Springer, 2011, a.462-476.
  • 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.
  • B. Xue, L. Cervante, L. Shang, W. N. Browne, and M. Zhang, "Multi-objective evolutionary algorithms for filter based feature selection in classification," Int. J. Artif. Intell. Tools, vol. 22, no. 4, 2013, Art. ID 1350024.
  • H. Xia, J. Zhuang, and D. Yu, "Multi-objective unsupervised fea-ture selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis," Neurocomputing, vol. 146.
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