IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i8p1878-d1124350.html
   My bibliography  Save this article

An AdaBoost Method with K′K-Means Bayes Classifier for Imbalanced Data

Author

Listed:
  • Yanfeng Zhang

    (Department of Statistics, Beijing Jiaotong University, Beijing 100044, China)

  • Lichun Wang

    (Department of Statistics, Beijing Jiaotong University, Beijing 100044, China)

Abstract

This article proposes a new AdaBoost method with k ′ k-means Bayes classifier for imbalanced data. It reduces the imbalance degree of training data through the k ′ k-means Bayes method and then deals with the imbalanced classification problem using multiple iterations with weight control, achieving a good effect without losing any raw data information or needing to generate more relevant data manually. The effectiveness of the proposed method is verified by comparing it with other traditional methods based on numerical experiments. In the NSL-KDD data experiment, the F-score values of each minority class are also greater than the other methods.

Suggested Citation

  • Yanfeng Zhang & Lichun Wang, 2023. "An AdaBoost Method with K′K-Means Bayes Classifier for Imbalanced Data," Mathematics, MDPI, vol. 11(8), pages 1-11, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1878-:d:1124350
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/8/1878/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/8/1878/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gao, Lu & Lu, Pan & Ren, Yihao, 2021. "A deep learning approach for imbalanced crash data in predicting highway-rail grade crossings accidents," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Jian Huang & Junyi Chai & Stella Cho, 2020. "Deep learning in finance and banking: A literature review and classification," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-24, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amin Aminimehr & Ali Raoofi & Akbar Aminimehr & Amirhossein Aminimehr, 2022. "A Comprehensive Study of Market Prediction from Efficient Market Hypothesis up to Late Intelligent Market Prediction Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 781-815, August.
    2. Mahsa Tavakoli & Rohitash Chandra & Fengrui Tian & Cristi'an Bravo, 2023. "Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams," Papers 2304.10740, arXiv.org, revised Sep 2023.
    3. Heyam H. Al-Baity, 2023. "The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
    4. Ni Zhan, 2021. "Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits," Papers 2101.09230, arXiv.org.
    5. Penghui Zhao & Jianxiao Ma & Chubo Xu & Chuwei Zhao & Zifan Ni, 2022. "Research on the Safety of the Left Hard Shoulder in a Multi-Lane Highway Based on Safety Performance Function," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    6. Sergio Consoli & Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Neural forecasting of the Italian sovereign bond market with economic news," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 197-224, December.
    7. Singh, Prashant & Pasha, Junayed & Moses, Ren & Sobanjo, John & Ozguven, Eren E. & Dulebenets, Maxim A., 2022. "Development of exact and heuristic optimization methods for safety improvement projects at level crossings under conflicting objectives," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    8. Jaydip Sen & Rajdeep Sen & Abhishek Dutta, 2021. "Machine Learning in Finance-Emerging Trends and Challenges," Papers 2110.11999, arXiv.org.
    9. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    10. Valentina ZOZULYA & Evgeny SOKOLOV & Evgeny KOSTYRIN & Sergey KOROLEV, 2021. "The effectiveness of applying beta-coefficient modifications when calculating returns on shares in Russian companies," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 12, pages 31-52, June.
    11. Yan, Dongyang & Li, Keping & Zhu, Qiaozhen & Liu, Yanyan, 2023. "A railway accident prevention method based on reinforcement learning – Active preventive strategy by multi-modal data," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    12. Kanzari, Dalel & Nakhli, Mohamed Sahbi & Gaies, Brahim & Sahut, Jean-Michel, 2023. "Predicting macro-financial instability – How relevant is sentiment? Evidence from long short-term memory networks," Research in International Business and Finance, Elsevier, vol. 65(C).
    13. Kouladoum, Jean-Claude & Wirajing, Muhamadu Awal Kindzeka & Nchofoung, Tii N., 2022. "Digital technologies and financial inclusion in Sub-Saharan Africa," Telecommunications Policy, Elsevier, vol. 46(9).
    14. Ricardo Cuervo, 2023. "Predictive AI for SME and Large Enterprise Financial Performance Management," Papers 2311.05840, arXiv.org.
    15. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
    16. Park, Chan Hee & Kim, Hyeongmin & Suh, Chaehyun & Chae, Minseok & Yoon, Heonjun & Youn, Byeng D., 2022. "A health image for deep learning-based fault diagnosis of a permanent magnet synchronous motor under variable operating conditions: Instantaneous current residual map," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    17. Fatemeh Mostofi & Vedat Toğan & Yunus Emre Ayözen & Onur Behzat Tokdemir, 2022. "Construction Safety Risk Model with Construction Accident Network: A Graph Convolutional Network Approach," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
    18. Weidong Chen & Xiaohui Yuan, 2021. "Financial inclusion in China: an overview," Frontiers of Business Research in China, Springer, vol. 15(1), pages 1-21, December.
    19. Ivan Jajić & Tomislav Herceg & Mirjana Pejić Bach, 2022. "Deployment of the Microeconomic Consumer Theory in the Artificial Neural Networks Modelling: Case of Organic Food Consumption," Mathematics, MDPI, vol. 10(17), pages 1-21, September.
    20. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1878-:d:1124350. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.