Abstract
Feature selection is a key step in classification task to prune out redundant or irrelevant information and improve the pattern recognition performance, but it is a challenging and complex combinatorial problem, especially in high dimensional feature selection. This paper proposes a self-adaptive strategy based differential evolution feature selection, abbreviated as SADEFS, in which the self-adaptive elimination and reproduction strategies are used to introduce superior features by considering their contributions in classification under historical records and to replace the poor performance features. The processes of the elimination and reproduction are self-adapted by leaning from their experiences to reduce search space and improve classification accuracy rate. Twelve high dimensional cancer micro-array benchmark datasets are introduced to verify the efficiency of SADEFS algorithm. The experiments indicate that SADEFS can achieve higher classification performance in comparison to the original DEFS algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics, p. 049901. Springer, New York (2006)
Bermingham, M.L., Pongwong, R., Spiliopoulou, A., Hayward, C., Rudan, I., Campbell, H.: Application of high-dimensional feature selection: evaluation for genomic prediction in man. Sci. Rep. 5, 10312 (2015)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(6), 1157–1182 (2003)
Khushaba, R.N., Al-Ani, A., AlSukker, A., Al-Jumaily, A.: A combined ant colony and differential evolution feature selection algorithm. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 1–12. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87527-7_1
Bidi, N., Elberrichi, Z.: Feature selection for text classification using genetic algorithms. In: International Conference on Modelling, Identification and Control, pp. 806–810. IEEE (2017)
Rashno, A., Nazari, B., Sadri, S., Saraee, M.: Effective pixel classification of mars images based on ant colony optimization feature selection and extreme learning machine. Neurocomputing 226(C), 66–79 (2017)
Chen, Q., Zhang, M., Xue, B.: Feature selection to improve generalization of genetic programming for high-dimensional symbolic regression. IEEE Trans. Evol. Comput. 21(5), 792–806 (2017)
Al-Ani, A., Alsukker, A., Khushaba, R.N.: Feature subset selection using differential evolution and a wheel based search strategy. Swarm Evol. Comput. 9, 15–26 (2013)
Khushaba, R.N., Al-Ani, A., Al-Jumaily, A.: Feature subset selection using differential evolution and a statistical repair mechanism. Expert Syst. Appl. 38(9), 11515–11526 (2011)
Bharathi, P.T., Subashini, P.: Differential evolution and genetic algorithm based feature subset selection for recognition of river ice types. J. Theoret. Appl. Inf. Technol. 67(1), 254–262 (2014)
Masood, A., Al-Jumaily, A.: Adaptive differential evolution based feature selection and parameter optimization for advised SVM classifier. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9489, pp. 401–410. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26532-2_44
Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)
Wang, H., Niu, B.: A novel bacterial algorithm with randomness control for feature selection in classification. Neurocomputing 228, 176–186 (2017)
Shi, Y.: Brain storm optimization algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011. LNCS, vol. 6728, pp. 303–309. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21515-5_36
Acknowledgment
This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71571120, 71471158, 61472257), Natural Science Foundation of Guangdong Province (2016A030310074), NTUT-SZU Joint Research Program (2018003), and Project supported by GDHVPS 2016.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Niu, B., Yang, X., Wang, H., Huang, K., Weng, SS. (2018). Feature Subset Selection Using a Self-adaptive Strategy Based Differential Evolution Method. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-93815-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93814-1
Online ISBN: 978-3-319-93815-8
eBook Packages: Computer ScienceComputer Science (R0)