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Genetic Programming for Ensemble Learning in Face Recognition

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Advances in Swarm Intelligence (ICSI 2022)

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

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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.

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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.

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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
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  2. Lianbo Ma
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  3. Qunfeng Liu
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  4. Nan Li
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  5. Yang Liu
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Corresponding author

Correspondence to Lianbo Ma .

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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

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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

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09725-6

  • Online ISBN: 978-3-031-09726-3

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