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Niching-Assisted Genetic Programming for Finding Multiple High-Quality Classifiers

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AI 2022: Advances in Artificial Intelligence (AI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13728))

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Abstract

Explainable artificial intelligence (XAI) is a recent research focus, aiming to gain trust in machine learning models with clear insights into how the models make certain predictions. Due to its ability to evolve potentially interpretable classifiers, genetic programming (GP) is generally well-suited to XAI. However, many learning algorithms including GP usually learn a single best model. In practice, the best model in terms of training classification accuracy/error rate may not be the most appropriate one from the perspective of a domain expert due to overfitting and limited data. Multiple explicit and high-quality classifiers with the same training performance are therefore needed to increase the chances that the generated models will be considered more reasonable to experts. Therefore, this study designs a niching-assisted GP approach for classification. The results show that the proposed method can significantly increase the classification accuracy on most of the tested datasets. Further analysis shows that the designed algorithm can find different GP programs with the same classification performance, providing good interpretability for classification tasks.

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Notes

  1. 1.

    Each individual or solution in GP is a classifier or a program with a tree representation. Therefore, this work treats individual, solution, classifier, tree, program in GP as the same.

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Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Peng Wang, Bing Xue & Mengjie Zhang

  2. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China

    Jing Liang

Authors
  1. Peng Wang
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  2. Bing Xue
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  3. Jing Liang
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  4. Mengjie Zhang
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Corresponding author

Correspondence to Peng Wang .

Editor information

Editors and Affiliations

  1. University of New South Wales, Sydney, NSW, Australia

    Haris Aziz

  2. University of Western Australia, Perth, WA, Australia

    Débora Corrêa

  3. University of Western Australia, Perth, WA, Australia

    Tim French

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Wang, P., Xue, B., Liang, J., Zhang, M. (2022). Niching-Assisted Genetic Programming for Finding Multiple High-Quality Classifiers. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_20

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  • DOI: https://doi.org/10.1007/978-3-031-22695-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22694-6

  • Online ISBN: 978-3-031-22695-3

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