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Multi-objective Feature Selection Based on Artificial Bee Colony for Hyperspectral Images

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

Abstract

Most of existing band selection methods based on evolutionary optimization emphasis on single indicator, ignoring the whole characteristic of hyperspectral data. This paper establishes a new multi-objective unsupervised band selection model by considering both the information contained in bands and the correlation between bands. And, an improved multi-objective feature selection based on artificial bee colony (MABCBS) is proposed by combining two new operators, i.e., multi-strategy hybrid search mechanism for employ bees and variable space crowing degree based search mechanism for onlooker bees. Appling in two typical data sets, experiment results verify the superiority of the proposed model and the proposed algorithm.

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References

  1. Hughes, G.: On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 14(1), 55–63 (1968)

    Article  Google Scholar 

  2. Li, J., et al.: Feature selection. ACM Comput. Surv. 50(6), 1–45 (2017)

    Article  Google Scholar 

  3. Martinez-Uso, A., Pla, F., Sotoca, J.M.: Clustering-based hyperspectral band selection using information measures. IEEE Trans. Geosci. Remote Sens. 45(12), 4158–4171 (2008)

    Article  Google Scholar 

  4. Cariou, C., Chehdi, K., Moan, S.: BandClust: an unsupervised band reduction method for hyperspectral remote sensing. IEEE Geosci. Remote Sens. Lett. 8(3), 565–569 (2011)

    Article  Google Scholar 

  5. Su, H., Yang, H., Du, Q.: Semi-supervised band clustering for dimensionality reduction of hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 8(6), 1135–1139 (2011)

    Article  Google Scholar 

  6. Jia, S., Zhen, J., Qian, Y.: Unsupervised band selection for hyperspectral imagery classification without manual band removal. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(2), 531–543 (2012)

    Article  Google Scholar 

  7. Cai, X., Mei, Z., Fan, Z., Zhang, Q.: A constrained decomposition approach with grids for evolutionary multi-objective optimization. IEEE Trans. Evol. Comput. 22(4), 99–104 (2018)

    Article  Google Scholar 

  8. Pan, L., He, C., Tian, Y., Wang, H., Zhang, X., Jin, Y.: A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Trans. Evol. Comput. 23(1), 74–88 (2018)

    Article  Google Scholar 

  9. He, C., Tian, Y., Jin, Y., Zhang, X., Pan, L.: A radial space division based evolutionary algorithm for many-objective optimization. Appl. Soft Comput. 61, 603–621 (2017)

    Article  Google Scholar 

  10. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Erciyes University, Kayseri (2005)

    Google Scholar 

  11. Chang, C., Wang, S.: Constrained band selection for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 45(12), 4158–4171 (2007)

    Article  Google Scholar 

  12. Martínez-Uso, A., Pla, F., Sotoca, J.M.: Clustering-based hyperspectral band selection using information measures. IEEE Trans. Geosci. Remote Sens. 45(12), 4158–4171 (2007)

    Article  Google Scholar 

  13. Xue, B., Zhang, M., Browne, W., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016)

    Article  Google Scholar 

  14. Zhang, Y., Song, X., Gong, D.: A return-cost-based binary firefly algorithm for feature selection. Inf. Sci. 418(419), 561–574 (2017)

    Article  Google Scholar 

  15. Zhang, Y., Gong, D., Cheng, J.: Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(1), 64–75 (2017)

    Article  Google Scholar 

  16. Zhang, Y., Cheng, S., Gong, D., Shi, Y., Zhao, X.: Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. Expert Syst. Appl. 137, 46–58 (2019)

    Article  Google Scholar 

  17. Zhang, Y., Li, H., Wang, Q.: A filter-based bare-bone particle swarm optimization algorithm for unsupervised feature selection. Appl. Intell. 49(8), 2889–2898 (2019)

    Article  Google Scholar 

  18. Xue, B., Zhang, M., Browne, W.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Cybern. 43(6), 1656–1671 (2013)

    Article  Google Scholar 

  19. Xue, Y., Xue, B., Zhang, M.: Self-adaptive particle swarm optimization for large-scale feature selection in classification. ACM Trans. Knowl. Discov. Data 13(5), 1–28 (2019)

    Article  Google Scholar 

  20. Paoli, A., Melgani, F., Pasolli, E.: Clustering of hyperspectral images based on multi-objective particle swarm optimization. IEEE Trans. Geosci. Remote Sens. 47(12), 4175–4188 (2009)

    Article  Google Scholar 

  21. Zhu, Y.: Hyperspectral band selection by multitask sparsity pursuit. IEEE Trans. Geosci. Remote Sens. 2(53), 631–644 (2015)

    Google Scholar 

  22. Zhang, M., Ma, J., Gong, M.: Unsupervised hyperspectral band selection by fuzzy clustering with particle swarm optimization. IEEE Geosci. Remote Sens. Lett. 14(5), 773–777 (2017)

    Article  Google Scholar 

  23. Feng, J., Jiao, L., Liu, F.: Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images. Pattern Recogn. 51(C), 295–309 (2016)

    Article  Google Scholar 

  24. Zhang, M., Gong, M., Chan, Y.: Hyperspectral band selection based on multi-objective optimization with high information and low redundancy. Appl. Soft Comput. 70, 604–621 (2018)

    Article  Google Scholar 

  25. Kiran, M., Hakli, H., Gunduz, M.: Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf. Sci. 300, 140–157 (2015)

    Article  MathSciNet  Google Scholar 

  26. Zhang, X., Tian, Y., Cheng, R.: An efficient approach to nondominated sorting for evolutionary multi-objective optimization. IEEE Trans. Evol. Comput. 19(2), 201–213 (2015)

    Article  Google Scholar 

  27. Deb, K., Pratap, A., Agarwal, S.: A fast and elitist multi-objective genetic algorithm NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  28. Xue, Y., Jiang, J., Zhao, B., Ma, T.: A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft. Comput. 22(9), 2935–2952 (2018)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China

    Chun-lin He, Yong Zhang & Dun-wei Gong

  2. School of Economics and Management, Nanjing Technology University, Nanjing, China

    Bin Wu

Authors
  1. Chun-lin He
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  2. Yong Zhang
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  3. Dun-wei Gong
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  4. Bin Wu
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Corresponding author

Correspondence to Yong Zhang .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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He, Cl., Zhang, Y., Gong, Dw., Wu, B. (2020). Multi-objective Feature Selection Based on Artificial Bee Colony for Hyperspectral Images. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_48

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_48

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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