A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 11 Issue 2
Feb.  2024

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
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.


  • loading
  • [1]
    S. Katoch, S. S. Chauhan, and V. Kumar, “A review on genetic algorithm: Past, present, and future,” Multimed. Tools Appl., vol. 80, no. 5, pp. 8091–8126, Feb. 2021. doi: 10.1007/s11042-020-10139-6
    Ş. Öztürk, R. Ahmad, and N. Akhtar, “Variants of artificial bee colony algorithm and its applications in medical image processing,” Appl. Soft Comput., vol. 97, p. 106799, Dec. 2020. doi: 10.1016/j.asoc.2020.106799
    W. Deng, J. Xu, H. Zhao, and Y. Song, “A novel gate resource allocation method using improved PSO-based QEA,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 3, pp. 1737–1745, Mar. 2022. doi: 10.1109/TITS.2020.3025796
    E. B. Tirkolaee, A. Goli, and G. W. Weber, “Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option,” IEEE Trans. Fuzzy Syst., vol. 28, no. 11, pp. 2772–2783, Nov. 2020. doi: 10.1109/TFUZZ.2020.2998174
    J. R. Albert, A. Sharma, B. Rajani, A. Mishra, A. Saxena, C. Nandagopal, and S. Mewada, “Investigation on load harmonic reduction through solar-power utilization in intermittent SSFI using particle swarm, genetic, and modified firefly optimization algorithms,” J. Intell. Fuzzy Syst., vol. 42, no. 4, pp. 4117–4133, Mar. 2022. doi: 10.3233/JIFS-212559
    R. Rajabioun, “Cuckoo optimization algorithm,” Appl. Soft Comput., vol. 11, no. 8, pp. 5508–5518, Dec. 2011. doi: 10.1016/j.asoc.2011.05.008
    J. Bi, H. Yuan, J. Zhai, M. Zhou, and H. V. Poor, “Self-adaptive bat algorithm with genetic operations,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1284–1294, Jul. 2022. doi: 10.1109/JAS.2022.105695
    S. Mirjalili, S. Saremi, S. M. Mirjalili, and L. D. S. Coelho, “Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization,” Expert Syst. Appl., vol. 47, pp. 106–119, Apr. 2016. doi: 10.1016/j.eswa.2015.10.039
    N. Hansen, S. D. Müller, and P. Koumoutsakos, “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES),” Evol. Comput., vol. 11, no. 1, pp. 1–18, Mar. 2003. doi: 10.1162/106365603321828970
    S. Han, K. Zhu, M. Zhou, X. Liu, H. Liu, Y. Al-Turki, and A. Abusorrah, “A novel multiobjective fireworks algorithm and its applications to imbalanced distance minimization problems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1476–1489, Aug. 2022. doi: 10.1109/JAS.2022.105752
    D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67–82, Apr. 1997. doi: 10.1109/4235.585893
    H. Zhu, G. Liu, M. Zhou, Y. Xie, and Q. Kang, “Dandelion algorithm with probability-based mutation,” IEEE Access, vol. 7, pp. 97974–97985, Jul. 2019. doi: 10.1109/ACCESS.2019.2927846
    C. Gong, S. Han, X. Li, L. Zhao, and X. Liu, “A new dandelion algorithm and optimization for extreme learning machine,” J. Exp. Theor. Artif. Intell., vol. 30, no. 1, pp. 39–52, 2018. doi: 10.1080/0952813X.2017.1413142
    S. Han and K. Zhu, “Fusion with distance-aware selection strategy for dandelion algorithm,” Knowl.-Based Syst., vol. 205, p. 106282, Oct. 2020. doi: 10.1016/j.knosys.2020.106282
    S. Han, K. Zhu, and R. Wang, “Improvement of evolution process of dandelion algorithm with extreme learning machine for global optimization problems,” Expert Syst. Appl., vol. 163, p. 113803, Jan. 2021. doi: 10.1016/j.eswa.2020.113803
    G.-B. Huang, Q.-Y. Zhu, and C. K. Siew, “Extreme learning machine: A new learning scheme of feedforward neural networks,” in Proc. IEEE Int. Joint Conf. Neural Networks, Budapest, Hungary, 2004, pp. 985–990.
    S. Han, K. Zhu, and M. Zhou, “Competition-driven dandelion algorithms with historical information feedback,” IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 2, pp. 966–979, Feb. 2022. doi: 10.1109/TSMC.2020.3010052
    J. Li, J. Q. Zhang, C. J. Jiang, and M. Zhou, “Composite particle swarm optimizer with historical memory for function optimization,” IEEE Trans. Cybern., vol. 45, no. 10, pp. 2350–2363, Oct. 2015. doi: 10.1109/TCYB.2015.2424836
    X. Liu and X. Qin, “A probability-based core dandelion guided dandelion algorithm and application to traffic flow prediction,” Eng. Appl. Artif. Intell., vol. 96, p. 103922, Nov. 2020. doi: 10.1016/j.engappai.2020.103922
    H. Zhu, G. Liu, M. Zhou, Y. Xie, A. Abusorrah, and Q. Kang, “Optimizing weighted extreme learning machines for imbalanced classification and application to credit card fraud detection,” Neurocomputing, vol. 407, pp. 50–62, Sept. 2020. doi: 10.1016/j.neucom.2020.04.078
    A. H. Hosseinian and V. Baradaran, “Detecting communities of workforces for the multi-skill resource-constrained project scheduling problem: A dandelion solution approach,” J. Ind. Syst. Eng., vol. 12, no. Special issue on Project Management and Control, pp. 72–99, Jan. 2019.
    V. Bolón-Canedo, N. Sánchez-Maroño, A. Alonso-Betanzos, J. M. Benítez, and F. Herrera, “A review of microarray datasets and applied feature selection methods,” Inf. Sci., vol. 282, pp. 111–135, Oct. 2014. doi: 10.1016/j.ins.2014.05.042
    Z. Sun, G. Bebis, and R. Miller, “Object detection using feature subset selection,” Pattern Recognit., vol. 37, no. 11, pp. 2165–2176, Nov. 2004. doi: 10.1016/j.patcog.2004.03.013
    B. Xue, M. Zhang, and W. N. Browne, “Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms,” Appl. Soft Comput., vol. 18, pp. 261–276, May 2014. doi: 10.1016/j.asoc.2013.09.018
    M. K. Masood, Y. C. Soh, and C. Jiang, “Occupancy estimation from environmental parameters using wrapper and hybrid feature selection,” Appl. Soft Comput., vol. 60, pp. 482–494, Nov. 2017. doi: 10.1016/j.asoc.2017.07.003
    Y. Xue, B. Xue, and M. Zhang, “Self-adaptive particle swarm optimization for large-scale feature selection in classification,” ACM Trans. Knowl. Discovery Data, vol. 13, no. 5, p. 50, Oct. 2019.
    R. Sagban, H. A. Marhoon, and R. Alubady, “Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care,” Int. J. Electr. Comput. Eng., vol. 10, no. 6, pp. 6655–6663, Dec. 2020.
    X. Li, S. Han, L. Zhao, C. Gong, and X. Liu, “New dandelion algorithm optimizes extreme learning machine for biomedical classification problems,” Comput. Intell. Neurosci., vol. 2017, p. 4523754, Sept. 2017.
    Q. Kang, L. Shi, M. Zhou, X. S. Wang, Q. D. Wu, and Z. Wei, “A distance-based weighted undersampling scheme for support vector machines and its application to imbalanced classification,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 9, pp. 4152–4165, Sept. 2018. doi: 10.1109/TNNLS.2017.2755595
    L. Zheng, G. Liu, C. Yan, and C. Jiang, “Transaction fraud detection based on total order relation and behavior diversity,” IEEE Trans. Comput. Soc. Syst., vol. 5, no. 3, pp. 796–806, Sept. 2018. doi: 10.1109/TCSS.2018.2856910
    H. Liu, M. Zhou, and Q. Liu, “An embedded feature selection method for imbalanced data classification,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 703–715, May 2019. doi: 10.1109/JAS.2019.1911447
    W. Zong, G.-B. Huang, and Y. Chen, “Weighted extreme learning machine for imbalance learning,” Neurocomputing, vol. 101, pp. 229–242, Feb. 2013. doi: 10.1016/j.neucom.2012.08.010
    H. Yu, C. Sun, X. Yang, S. Zheng, Q. Wang, and X. Xi, “LW-ELM: A fast and flexible cost-sensitive learning framework for classifying imbalanced data,” IEEE Access, vol. 6, pp. 28488–28500, May 2018. doi: 10.1109/ACCESS.2018.2839340
    J. J. Liang, B. Y. Qu, P. N. Suganthan, and A. G. Hernández-Díaz, “Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization,” Comput. Intell. Lab., Zhengzhou Univ., Zhengzhou, China and Nanyang Technol. Univ., Singapore, Tech. Rep., Jan. 2013.
    D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,” J. Global Optim., vol. 39, no. 3, pp. 459–471, Nov. 2007. doi: 10.1007/s10898-007-9149-x
    M. Zambrano-Bigiarini, M. Clerc, and R. Rojas, “Standard particle swarm optimisation 2011 at CEC-2013: A baseline for future PSO improvements,” in Proc. IEEE Congr. Evolutionary Computation, Cancun, Mexico, 2013, pp. 2337–2344.
    R. Storn and K. Price, “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces,” J. Global Optim., vol. 11, no. 4, pp. 341–359, Dec. 1997. doi: 10.1023/A:1008202821328
    N. Hansen and A. Ostermeier, “Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation,” in Proc. IEEE Int. Conf. Evolutionary Computation, Nagoya, Japan, 1996, pp. 312–317.
    J. M. Lobo, A. Jiménez-Valverde, and R. Real, “AUC: A misleading measure of the performance of predictive distribution models,” Global Ecol. Biogeogr., vol. 17, no. 2, pp. 145–151, Mar. 2008. doi: 10.1111/j.1466-8238.2007.00358.x
    S. Han, K. Zhu, M. Zhou, H. Alhumade, and A. Abusorrah, “Locating multiple equivalent feature subsets in feature selection for imbalanced classification,” IEEE Trans. Knowl. Data Eng., vol. 35, no. 9, pp. 9195–9209, Sept. 2023. doi: 10.1109/TKDE.2022.3222047
    Y. Xie, G. Liu, C. Yan, C. Jiang, M. Zhou, and M. Li, “Learning transactional behavioral representations for credit card fraud detection,” IEEE Trans. Neural Netw. Learn. Syst., 2022. DOI: 10.1109/TNNLS.2022.3208967
    Y. Xie, G. Liu, C. Yan, C. Jiang, and M. Zhou, “Time-aware attention-based gated network for credit card fraud detection by extracting transactional behaviors,” IEEE Trans. Comput. Soc. Syst., vol. 10, no. 3, pp. 1004–1016, Jun. 2023. doi: 10.1109/TCSS.2022.3158318
    S. Han, K. Zhu, M. Zhou, and X. Cai, “Competition-driven multimodal multiobjective optimization and its application to feature selection for credit card fraud detection,” IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 12, pp. 7845–7857, Dec. 2022. doi: 10.1109/TSMC.2022.3171549
    S. Han, K. Zhu, M. Zhou, and X. Liu, “Joint deployment optimization and flight trajectory planning for UAV assisted IoT data collection: A bilevel optimization approach,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 11, pp. 21492–21504, Nov. 2022. doi: 10.1109/TITS.2022.3180288
    Z. Huang, Y. Liu, C. Zhan, C. Lin, W. Cai, and Y. Chen, “A novel group recommendation model with two-stage deep learning,” IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 9, pp. 5853–5864, Sept. 2022. doi: 10.1109/TSMC.2021.3131349
    M. Cui, L. Li, M. Zhou, and A. Abusorrah, “Surrogate-assisted autoencoder-embedded evolutionary optimization algorithm to solve high-dimensional expensive problems,” IEEE Trans. Evol. Comput., vol. 26, no. 4, pp. 676–689, Aug. 2022. doi: 10.1109/TEVC.2021.3113923
    M. Cui, L. Li, M. Zhou, J. Li, A. Abusorrah, and K. Sedraoui, “A Bi-population cooperative optimization algorithm assisted by an autoencoder for medium-scale expensive problems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 1952–1966, Nov. 2022. doi: 10.1109/JAS.2022.105425
    Z. Zhao, S. Liu, M. Zhou, and A. Abusorrah, “Dual-objective mixed integer linear program and memetic algorithm for an industrial group scheduling problem,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1199–1209, Jun. 2021. doi: 10.1109/JAS.2020.1003539
    X. Zhu and M. Zhou, “Multiobjective optimized deployment of edge-enabled wireless visual sensor networks for target coverage,” IEEE Internet Things J., vol. 10, no. 17, pp. 15325–15337, Sept. 2023. doi: 10.1109/JIOT.2023.3262849
    D. Li, Q. Wu, M. Zhou, and F. Luo, “HHFS: A hybrid hierarchical feature selection method for ageing gene classification,” IEEE Trans. Cognit. Dev. Syst., vol. 15, no. 2, pp. 690–699, Jun. 2023. doi: 10.1109/TCDS.2022.3176548
    H. Liu, M. Zhou, X. Lu, A. Abusorrah, and Y. Al-Turki, “Analysis of evolutionary social media activities: Pre-vaccine and post-vaccine emergency use,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 1090–1092, Apr. 2023.
    W. Duo, M. Zhou, and A. Abusorrah, “A survey of cyber attacks on cyber physical systems: Recent advances and challenges,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 784–800, May 2022. doi: 10.1109/JAS.2022.105548
    X. Luo, Y. Yuan, S. Chen, N. Zeng, and Z. Wang, “Position-transitional particle swarm optimization-incorporated latent factor analysis,” IEEE Trans. Knowl. Data Eng., vol. 34, no. 8, pp. 3958–3970, Aug. 2022. doi: 10.1109/TKDE.2020.3033324
    D. Wu, Y. He, X. Luo, and M. Zhou, “A latent factor analysis-based approach to online sparse streaming feature selection,” IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 11, pp. 6744–6758, Nov. 2022. doi: 10.1109/TSMC.2021.3096065
    X. Luo, H. Wu, and Z. Li, “Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors,” IEEE Trans. Knowl. Data Eng., vol. 35, no. 6, pp. 6148–6166, Jun. 2023.
  • JAS-2023-0613-supp.pdf


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(13)

    Article Metrics

    Article views (227) PDF downloads(65) Cited by()


    • 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


    DownLoad:  Full-Size Img  PowerPoint