Skip to main content
Springer Nature Link
Log in

Genetic Programming for Ensemble Learning in Face Recognition

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13345))

Included in the following conference series:

Abstract

Ensemble learning has recently been explored to achieve a better generalization ability than a single base learner through combining results of multiple base learners. Genetic programming (GP) can be used to design ensemble learning via different strategies. However, the challenge remains to automatically design an ensemble learning model due to complex search space. In this paper, we propose a new automated ensemble learning framework, based on GP for face recognition, called Evolving Genetic Programming Ensemble Learning (EGPEL). This method integrates feature extraction, base learner selection, and learner hyperparameter optimization, into several program trees. To this end, multiple program trees, a base learner set, and a hyperparameter set are developed in EGPEL. Meanwhile, an evolutionary approach to results integration is proposed. The performance of EGPEL is verified on face benchmark datasets of difficulty and compared with a large number of commonly used peer competitors, including state-of-the-art competitors. The results show that EGPEL performs better than most competitive ensemble learning methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Dong, X., Yu, Z., Cao, W., et al.: A survey on ensemble learning. Front. Compt. Sci. 14(2), 241–258 (2020). https://doi.org/10.1007/s11704-019-8208-z

    Article  Google Scholar 

  2. Zhang, C., Ma, Y. (eds.): Ensemble Machine Learning: Methods and Applications. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4419-9326-7

    Book  MATH  Google Scholar 

  3. Gomes, H.M., Barddal, J.P., Enembreck, F., et al.: A survey on ensemble learning for data stream classification. ACM Comput. Surv. (CSUR) 50(2), 1–36 (2017)

    Article  Google Scholar 

  4. Bi, Y., Xue, B., Zhang, M.: An automated ensemble learning framework using genetic programming for image classification. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 365–373 (2019)

    Google Scholar 

  5. Hutter, F., Kotthoff, L., Vanschoren, J.: Automated Machine Learning: Methods, Systems, Challenges. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-05318-5

    Book  Google Scholar 

  6. Ma, L., Huang, M., Yang, S., et al.: An adaptive localized decision variable analysis approach to large-scale multiobjective and many-objective optimization. IEEE Trans. Cybern. (2021). https://doi.org/10.1109/TCYB.2020.3041212

  7. Ma, L., Li, N., Guo, Y., et al.: Learning to optimize: reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system. IEEE Trans. Cybern. (2021). https://doi.org/10.1109/TCYB.2021.3086501

  8. Ma, L., Wang, X., Huang, M., et al.: Two-level master–slave RFID networks planning via hybrid multiobjective artificial bee colony optimizer. IEEE Trans. Syst. Man Cybern. Syst. 49(5), 861–880 (2019)

    Article  Google Scholar 

  9. Ma, L., Wang, X., Huang, M., et al.: A novel evolutionary root system growth algorithm for solving multi-objective optimization problems. Appl. Soft Comput. 57, 379–398 (2017)

    Article  Google Scholar 

  10. Chen, H., Zhu, Y., Hu, K., et al.: Bacterial colony foraging algorithm: combining chemotaxis, cell-to-cell communication, and self-adaptive strategy. Inf. Sci. 273, 73–100 (2014)

    Article  MathSciNet  Google Scholar 

  11. Su, W., Ma, L., Hu, K., et al.: A research on integrated application of RFID-based lean manufacturing. In: 2009 Chinese Control and Decision Conference, pp. 5781–5784. IEEE (2009)

    Google Scholar 

  12. Liu, Y., Ma, L., Yang, G.: A survey of artificial bee colony algorithm. In: 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 1510–1515. IEEE (2017)

    Google Scholar 

  13. Ma, L., Li, X., Gao, T., et al.: Indicator-based multi-objective bacterial foraging algorithm with adaptive searching mechanism. In: International Conference on Bio-Inspired Computing: Theories and Applications, pp. 271–277. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-3614-9_33

  14. Chandra, A., Yao, X.: Ensemble learning using multi-objective evolutionary algorithms. J. Math. Model Algorithms 5(4), 417–445 (2006). https://doi.org/10.1007/s10852-005-9020-3

    Article  MathSciNet  MATH  Google Scholar 

  15. Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(2), 121–144 (2009)

    Article  Google Scholar 

  16. Bi, Y., Xue, B., Zhang, M.: Dual-tree genetic programming for few-shot image classification. IEEE Trans. Evol. Comput. PP(99), 1 (2021)

    Google Scholar 

  17. Alam, K.M., Siddique, N., Adeli, H.: A dynamic ensemble learning algorithm for neural networks. Neural Comput. Appl. 32(12), 8675–8690 (2020). https://doi.org/10.1007/s00521-019-04359-7

    Article  Google Scholar 

  18. Wang, Y., Wang, D., Geng, N., et al.: Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection. Appl. Soft Comput. 77, 188–204 (2019)

    Article  Google Scholar 

  19. Cui, S., Yin, Y., Wang, D., et al.: A stacking-based ensemble learning method for earthquake casualty prediction. Appl. Soft Comput. 101, 107038 (2021)

    Article  Google Scholar 

  20. Wang, Y., Liu, S., Li, S., et al.: Stacking-based ensemble learning of self-media data for marketing intention detection. Future Internet 11(7), 155 (2019)

    Article  Google Scholar 

  21. Zhou, Z.H.: Ensemble Learning. Machine Learning, pp. 181–210. Springer, Singapore (2021)

    Google Scholar 

  22. Awad, A., Bader-El-Den, M., McNicholas, J., et al.: Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. Int. J. Med. Inf. 108, 185–195 (2017)

    Article  Google Scholar 

  23. Mohammed, A.A., Yaqub, W., Aung, Z.: Probabilistic forecasting of solar power: an ensemble learning approach. In: International Conference on Intelligent Decision Technologies, pp. 449–458. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-19857-6_38

  24. Wang, S., Zhu, J., Yin, Y., et al.: Interpretable multi-modal stacking-based ensemble learning method for real estate appraisal. IEEE Trans. Multimedia PP(99), 1 (2021)

    Google Scholar 

  25. Aboneh, T., Rorissa, A., Srinivasagan, R.: Stacking-based ensemble learning method for multi-spectral image classification. Technologies 10(1), 17 (2022)

    Article  Google Scholar 

  26. El-Rashidy, N., Abuhmed, T., Alarabi, L., et al.: Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning. Neural Comput. Appl. 34(5), 3603–3632 (2022). https://doi.org/10.1007/s00521-021-06631-1

    Article  Google Scholar 

  27. Madhu, G., Bharadwaj, B.L., Boddeda, R., et al.: Deep stacked ensemble learning model for COVID-19 classification. Comput. Mater. Continua, 5467–5486 (2022)

    Google Scholar 

  28. Obasi, T.G., Shafiq, M.O.: CARD-B: a stacked ensemble learning technique for classification of encrypted network traffic. Comput. Commun. (2022). https://doi.org/10.1016/j.comcom.2022.02.006

    Article  Google Scholar 

  29. Dong, Y., Zhang, H., Wang, C., et al.: Wind power forecasting based on stacking ensemble model, decomposition and intelligent optimization algorithm. Neurocomputing 462, 169–184 (2021)

    Article  Google Scholar 

  30. Ma, L., Cheng, S., Shi, Y.: Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Trans. Syst. Man Cybern. Syst. 51(11), 6723–6742 (2020)

    Article  Google Scholar 

  31. Ma, L., Wang, X., Wang, X., et al.: TCDA: truthful combinatorial double auctions for mobile edge computing in industrial internet of things. IEEE Trans. Mobile Comput. (2021)

    Google Scholar 

  32. Santoso, L., Singh, B., Rajest, S., et al.: A genetic programming approach to binary classification problem. EAI Endorsed Trans. Energy Web 8(31), e11 (2020)

    Google Scholar 

  33. Devarriya, D., Gulati, C., Mansharamani, V., et al.: Unbalanced breast cancer data classification using novel fitness functions in genetic programming. Expert Syst. Appl. 140, 112866 (2020)

    Article  Google Scholar 

  34. Tran, C.T., Zhang, M., Xue, B., et al.: Genetic programming with interval functions and ensemble learning for classification with incomplete data. In: Australasian Joint Conference on Artificial Intelligence, pp. 577–589. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03991-2_53

  35. Park, J., Mei, Y., Nguyen, S., et al.: An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling. Appl. Soft Comput. 63, 72–86 (2018)

    Article  Google Scholar 

  36. Chen, Q., Xue, B., Zhang, M.: Instance based transfer learning for genetic programming for symbolic regression. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 3006–3013. IEEE (2019)

    Google Scholar 

  37. Dick, G., Owen, C.A., Whigham, P.A.: Evolving bagging ensembles using a spatially-structured niching method. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 418–425 (2018)

    Google Scholar 

  38. Karakatič, S., Podgorelec, V.: Building boosted classification tree ensemble with genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 165–166 (2018)

    Google Scholar 

  39. Recht, B., Roelofs, R., Schmidt, L., et al.: Do imagenet classifiers generalize to imagenet? In: International Conference on Machine Learning. PMLR, pp. 5389–5400 (2019)

    Google Scholar 

Download references

Acknowledgments

This research was funded by the National Key R&D Program of China under Grant 2018YFB2003502, the Fundamental Research Funds for the Central Universities, and Guangdong Universities’ Special Projects in Key Fields of Natural Science under Grant 2019KZDZX1005.

Author information

Authors and Affiliations

  1. College of Software, Northeastern University, Shenyang, China

    Tian Zhang, Lianbo Ma & Nan Li

  2. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

    Lianbo Ma

  3. School of Computer Science and Technology, Dongguan University of Technology, Guangdong, China

    Qunfeng Liu

  4. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China

    Yang Liu

Authors
  1. Tian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lianbo Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qunfeng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lianbo Ma .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, T., Ma, L., Liu, Q., Li, N., Liu, Y. (2022). Genetic Programming for Ensemble Learning in Face Recognition. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13345. Springer, Cham. https://doi.org/10.1007/978-3-031-09726-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-09726-3_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09725-6

  • Online ISBN: 978-3-031-09726-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics