IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2024i1p103-d1556281.html
   My bibliography  Save this article

Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group

Author

Listed:
  • Meng Han

    (School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China)

  • Shurong Yang

    (School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China)

  • Hongxin Wu

    (School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China)

  • Jian Ding

    (School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China)

Abstract

Ensemble classification is widely used in multi-label algorithms, and it can be divided into homogeneous ensembles and heterogeneous ensembles according to classifier types. A heterogeneous ensemble can generate classifiers with better diversity than a homogeneous ensemble and improve the performance of classification results. An Adaptive Heterogeneous Classifier Group (AHCG) algorithm is proposed. The AHCG first proposes the concept of a Heterogeneous Classifier Group (HCG); that is, two groups of different ensemble classifiers are used in the testing and training phases. Secondly, the Adaptive Selection Strategy (ASS) is proposed, which can select the ensemble classifiers to be used in the test phase. The least squares method is used to calculate the weights of the base classifiers for the in-group classifiers and dynamically update the base classifiers according to the weights. A large number of experiments on seven datasets show that this algorithm has better performance than most existing ensemble classification algorithms in terms of its accuracy, example-based F1 value, micro-averaged F1 value, and macro-averaged F1 value.

Suggested Citation

  • Meng Han & Shurong Yang & Hongxin Wu & Jian Ding, 2024. "Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group," Mathematics, MDPI, vol. 13(1), pages 1-20, December.
    • Handle: RePEc:gam:jmathe:v:13:y:2024:i:1:p:103-:d:1556281
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/1/103/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/1/103/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Roy, Atin & Chakraborty, Subrata, 2023. "Support vector machine in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    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. Jiaan Zhang & Wenxin Liu & Zhenzhen Wang & Ruiqing Fan, 2024. "Electric Vehicle Power Consumption Modelling Method Based on Improved Ant Colony Optimization-Support Vector Regression," Energies, MDPI, vol. 17(17), pages 1-17, August.
    2. Zaher Salah & Hamza Abu Owida & Esraa Abu Elsoud & Esraa Alhenawi & Suhaila Abuowaida & Nawaf Alshdaifat, 2024. "An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form," Future Internet, MDPI, vol. 16(11), pages 1-24, November.
    3. Zhao, Enyong & Wang, Qihan & Alamdari, Mehrisadat Makki & Gao, Wei, 2023. "Advanced virtual model assisted most probable point capturing method for engineering structures," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Mehmet Kayakuş & Fatma Yiğit Açikgöz & Mirela Nicoleta Dinca & Onder Kabas, 2024. "Sustainable Brand Reputation: Evaluation of iPhone Customer Reviews with Machine Learning and Sentiment Analysis," Sustainability, MDPI, vol. 16(14), pages 1-17, July.
    5. Zheng, Weiming & Yuan, Xiukai & Bao, Xiya & Dong, Yiwei, 2025. "Adaptive support vector machine for time-variant failure probability function estimation," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    6. Shan, Shan, 2024. "Unveiling Socioeconomic Factors Shaping Global Food Prices and Security: A Machine Learning Approach," Asian Journal of Agriculture and Development, Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA), vol. 21(2), December.
    7. Yu, Shui & Ren, Yuyao & Wu, Xiao & Guo, Peng & Li, Yun, 2024. "Dynamic pruning-based Bayesian support vector regression for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    8. Yanlin Wang & Zhijun Cheng & Zichen Wang, 2024. "Multi-Output Bayesian Support Vector Regression Considering Dependent Outputs," Mathematics, MDPI, vol. 12(18), pages 1-20, September.
    9. Yu, Shui & Wu, Xiao & Zhao, Dongyu & Li, Yun, 2024. "A two-level surrogate framework for demand-objective time-variant reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    10. Zhao, Qiangqiang & Duan, Jinyan & Jia, Kang & Hong, Jun, 2025. "PLIC-FSR-SYS: System reliability analysis based on parallel learning of influential components with filtered sample region," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    11. Li, Guosheng & Ma, Shuaichao & Zhang, Dequan & Yang, Leping & Zhang, Weihua & Wu, Zeping, 2024. "An efficient sequential anisotropic RBF reliability analysis method with fast cross-validation and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    12. Xin, Fukang & Wang, Pan & Wang, Qirui & Li, Lei & Cheng, Lei & Lei, Huajin & Ma, Fangyun, 2024. "Parallel adaptive ensemble of metamodels combined with hypersphere sampling for rare failure events," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    13. Tian, Jilun & Zhang, Jiusi & Jiang, Yuchen & Wu, Shimeng & Luo, Hao & Yin, Shen, 2024. "A novel generalized source-free domain adaptation approach for cross-domain industrial fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    14. Fang, Chen & Xu, You-Lin & Li, Yongle & Li, Jinrong, 2024. "Serviceability analysis of sea-crossing bridges under correlated wind and wave loads," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    15. Ji, Haodong & Lyu, Yuhui & Tian, Zushi & Ye, Hailong, 2025. "Assessment of corrosion probability of steel in mortars using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    16. Li, Chen & Wen, Jiong-Ran & Wan, Jing & Taylan, Osman & Fei, Cheng-Wei, 2024. "Adaptive directed support vector machine method for the reliability evaluation of aeroengine structure," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    17. Pei, Pei & Zhou, Tong, 2023. "One-step look-ahead policy for active learning reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    18. Ouyang, Linhan & Che, Yushuai & Park, Chanseok & Chen, Yuejian, 2024. "A novel active learning Gaussian process modeling-based method for time-dependent reliability analysis considering mixed variables," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

    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:13:y:2024:i:1:p:103-:d:1556281. 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.