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Volume 11 Issue 2
Feb.  2024

IEEE/CAA Journal of Automatica Sinica

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H. Zhu, M. C. Zhou, Y. Xie, and  A. Albeshri,  “A self-adapting and efficient dandelion algorithm and its application to feature selection for credit card fraud detection,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 377–390, Feb. 2024. doi: 10.1109/JAS.2023.124008
Citation: H. Zhu, M. C. Zhou, Y. Xie, and  A. Albeshri,  “A self-adapting and efficient dandelion algorithm and its application to feature selection for credit card fraud detection,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 377–390, Feb. 2024. doi: 10.1109/JAS.2023.124008

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

doi: 10.1109/JAS.2023.124008
Funds:  This work was supported by the Institutional Fund Projects (IFPIP-1481-611-1443), the Key Projects of Natural Science Research in Anhui Higher Education Institutions (2022AH051909), the Provincial Quality Project of Colleges and Universities in Anhui Province (2022sdxx020, 2022xqhz044), and Bengbu University 2021 High-Level Scientific Research and Cultivation Project (2021pyxm04)
More Information
  • A dandelion algorithm (DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA, which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA’s parameters and simplify DA’s structure. Only the normal sowing operator is retained; while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection (CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

     

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    Highlights

    • Proposing a self-adapting and efficient dandelion algorithm
    • Developing an adaptive seeding radius strategy to reduce the number of DA’s parameters
    • Applying the proposed algorithm to feature selection for accurate and fast credit card fraud detection

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