IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i2p272-d1318993.html
   My bibliography  Save this article

Predicting Critical Path of Labor Dispute Resolution in Legal Domain by Machine Learning Models Based on SHapley Additive exPlanations and Soft Voting Strategy

Author

Listed:
  • Jianhua Guan

    (National Center for Applied Mathematics in Hunan, Xiangtan University, Xiangtan 411105, China
    Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China)

  • Zuguo Yu

    (National Center for Applied Mathematics in Hunan, Xiangtan University, Xiangtan 411105, China
    Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China)

  • Yongan Liao

    (Faculty of Law, Xiangtan University, Xiangtan 411105, China)

  • Runbin Tang

    (School of Mathematics Science, Chongqing Normal University, Chongqing 401331, China)

  • Ming Duan

    (Faculty of Law, Xiangtan University, Xiangtan 411105, China)

  • Guosheng Han

    (National Center for Applied Mathematics in Hunan, Xiangtan University, Xiangtan 411105, China
    Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China)

Abstract

The labor dispute is one of the most common civil disputes. It can be resolved in the order of the following steps, which include mediation in arbitration, arbitration award, first-instance mediation, first-instance judgment, and second-instance judgment. The process can cease at any step when it is successfully resolved. In recent years, due to the increasing rights awareness of employees, the number of labor disputes has been rising annually. However, resolving labor disputes is time-consuming and labor-intensive, which brings a heavy burden to employees and dispute resolution institutions. Using artificial intelligence algorithms to identify and predict the critical path of labor dispute resolution is helpful for saving resources and improving the efficiency of, and reducing the cost of dispute resolution. In this study, a machine learning approach based on Shapley Additive exPlanations (SHAP) and a soft voting strategy is applied to predict the critical path of labor dispute resolution. We name our approach LDMLSV (stands for Labor Dispute Machine Learning based on SHapley additive exPlanations and Voting). This approach employs three machine learning models (Random Forest, Extra Trees, and CatBoost) and then integrates them using a soft voting strategy. Additionally, SHAP is used to explain the model and analyze the feature contribution. Based on the ranking of feature importance obtained from SHAP and an incremental feature selection method, we obtained an optimal feature subset comprising 33 features. The LDMLSV achieves an accuracy of 0.90 on this optimal feature subset. Therefore, the proposed approach is a highly effective method for predicting the critical path of labor dispute resolution.

Suggested Citation

  • Jianhua Guan & Zuguo Yu & Yongan Liao & Runbin Tang & Ming Duan & Guosheng Han, 2024. "Predicting Critical Path of Labor Dispute Resolution in Legal Domain by Machine Learning Models Based on SHapley Additive exPlanations and Soft Voting Strategy," Mathematics, MDPI, vol. 12(2), pages 1-17, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:272-:d:1318993
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/2/272/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/2/272/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tao Huang & WeiRen Cui & LeLe Hu & KaiYan Feng & Yi-Xue Li & Yu-Dong Cai, 2009. "Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-7, 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. Tao Huang & Ping Wang & Zhi-Qiang Ye & Heng Xu & Zhisong He & Kai-Yan Feng & LeLe Hu & WeiRen Cui & Kai Wang & Xiao Dong & Lu Xie & Xiangyin Kong & Yu-Dong Cai & Yixue Li, 2010. "Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties," PLOS ONE, Public Library of Science, vol. 5(7), pages 1-7, July.
    2. Bi-Qing Li & Tao Huang & Jian Zhang & Ning Zhang & Guo-Hua Huang & Lei Liu & Yu-Dong Cai, 2013. "An Ensemble Prognostic Model for Colorectal Cancer," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.
    3. Lu-Lu Zheng & Shen Niu & Pei Hao & KaiYan Feng & Yu-Dong Cai & Yixue Li, 2011. "Prediction of Protein Modification Sites of Pyrrolidone Carboxylic Acid Using mRMR Feature Selection and Analysis," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-11, December.
    4. Yu-Dong Cai & Tao Huang & Kai-Yan Feng & Lele Hu & Lu Xie, 2010. "A Unified 35-Gene Signature for both Subtype Classification and Survival Prediction in Diffuse Large B-Cell Lymphomas," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-8, September.
    5. Lei Chen & Chen Chu & Xiangyin Kong & Guohua Huang & Tao Huang & Yu-Dong Cai, 2015. "A Hybrid Computational Method for the Discovery of Novel Reproduction-Related Genes," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-15, March.
    6. Tao Huang & Lei Chen & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Classification and Analysis of Regulatory Pathways Using Graph Property, Biochemical and Physicochemical Property, and Functional Property," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-11, September.
    7. Le-Le Hu & Shen Niu & Tao Huang & Kai Wang & Xiao-He Shi & Yu-Dong Cai, 2010. "Prediction and Analysis of Protein Hydroxyproline and Hydroxylysine," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-8, December.

    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:12:y:2024:i:2:p:272-:d:1318993. 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.