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Metaheuristics for Finding Multiple Solutions pp 259–285Cite as

Grammar-Based Multi-objective Genetic Programming with Token Competition and Its Applications in Financial Fraud Detection

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Abstract

In this study, we propose a new approach based on Grammar-based Genetic Programming (GBGP), token competition, multi-objective optimization, and ensemble learning for solving Financial Fraud Detection (FFD) problems. Token competition is a niching technique to maintain diversity among individuals. It can be used to adjust the objective values of each individual, and the individuals with similar objective values but different meanings are separated. Financial fraud is a serious problem that often produces destructive results in the world and it is exacerbating swiftly in many countries. It refers to many activities including credit card fraud, money laundering, insurance fraud, corporate fraud, etc. The major consequences of financial fraud are loss of billions of dollars each year, investor confidence, and corporate reputation. Therefore, a research area called FFD is obligatory, in order to prevent the destructive results caused by financial fraud. We comprehensively compare the proposed approach with Logistic Regression, Neural Networks, Support Vector Machine, Bayesian Networks, Decision Trees, AdaBoost, Bagging, and LogitBoost on four FFD datasets including two real-life datasets. The experimental results showed the effectiveness of the new approach. It outperforms existing data mining methods in different aspects.

Keywords

  • Grammar-based genetic programming
  • Token competition
  • Financial fraud detection
  • Multi-objective optimization

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Acknowledgements

This research is supported by the LEO Dr. David P. Chan Institute of Data Science and the General Research Fund LU310111 from the Research Grant Council of the Hong Kong Special Administrative Region.

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Authors and Affiliations

  1. Department of Computing and Decision Sciences, Lingnan University, Tuen Mun, Hong Kong

    Haibing Li & Man-Leung Wong

Authors
  1. Haibing Li
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  2. Man-Leung Wong
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Correspondence to Man-Leung Wong .

Editor information

Editors and Affiliations

  1. LIACS, Universiteit Leiden, Leiden, The Netherlands

    Assist. Prof. Mike Preuss

  2. The Signal Group, Athens, Greece

    Dr. Michael G. Epitropakis

  3. Computer Science and Software Engineering, RMIT University, Melbourne, VIC, Australia

    Prof. Xiaodong Li

  4. Department of Computer Science, University of Exeter, Exeter, UK

    Prof. Jonathan E. Fieldsend

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Li, H., Wong, ML. (2021). Grammar-Based Multi-objective Genetic Programming with Token Competition and Its Applications in Financial Fraud Detection. In: Preuss, M., Epitropakis, M.G., Li, X., Fieldsend, J.E. (eds) Metaheuristics for Finding Multiple Solutions. Natural Computing Series. Springer, Cham. https://doi.org/10.1007/978-3-030-79553-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-79553-5_11

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