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<p>Contract disputes in civil and commercial projects pose significant financial, legal, and operational challenges, creating a need for reliable data-driven dispute risk assessment methods. The research presents a proof-of-concept predictive and prescriptive analytics framework based on a Genetic Algorithm optimized Weighted Least Squares Support Vector Machine (GA-WLSSVM) for contract dispute risk prediction. The proposed approach integrates adaptive sample weighting with GA-based parameter optimization to improve classification robustness, particularly under class imbalance conditions. Experiments are conducted on a publicly available Kaggle civil and commercial contract dispute risk dataset, consisting of numerical and categorical attributes. The dataset is partitioned using a stratified 80/20 train–test split with fixed randomness to ensure reproducibility. GA optimization is performed on the training subset to determine optimal penalty and kernel parameters, while final performance is evaluated on a held-out test set. The proposed GA-WLSSVM achieves best optimized validation accuracy of 0.94 and F1-score of 0.91. In addition to predictive modeling, exploratory, model-derived analysis of dominant risk factors is used to generate indicative intervention insights, rather than deployment-validated optimization strategies. The results demonstrate methodological effectiveness within the experimental dataset, supporting the feasibility of the proposed approach. However, the empirical evaluation is limited to a single benchmark dataset with restricted attribute diversity, and the intervention analysis is based on model interpretation rather than real-world implementation. Consequently, the findings should be viewed as dataset-specific proof-of-concept evidence, with further validation on independent, real-world, and multi-jurisdictional contract datasets required before practical adoption in operational decision-support systems.</p>

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Risk prediction and intervention strategies for civil and commercial contract disputes based on genetic algorithms and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月7日

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  • Lin Duan

摘要

Contract disputes in civil and commercial projects pose significant financial, legal, and operational challenges, creating a need for reliable data-driven dispute risk assessment methods. The research presents a proof-of-concept predictive and prescriptive analytics framework based on a Genetic Algorithm optimized Weighted Least Squares Support Vector Machine (GA-WLSSVM) for contract dispute risk prediction. The proposed approach integrates adaptive sample weighting with GA-based parameter optimization to improve classification robustness, particularly under class imbalance conditions. Experiments are conducted on a publicly available Kaggle civil and commercial contract dispute risk dataset, consisting of numerical and categorical attributes. The dataset is partitioned using a stratified 80/20 train–test split with fixed randomness to ensure reproducibility. GA optimization is performed on the training subset to determine optimal penalty and kernel parameters, while final performance is evaluated on a held-out test set. The proposed GA-WLSSVM achieves best optimized validation accuracy of 0.94 and F1-score of 0.91. In addition to predictive modeling, exploratory, model-derived analysis of dominant risk factors is used to generate indicative intervention insights, rather than deployment-validated optimization strategies. The results demonstrate methodological effectiveness within the experimental dataset, supporting the feasibility of the proposed approach. However, the empirical evaluation is limited to a single benchmark dataset with restricted attribute diversity, and the intervention analysis is based on model interpretation rather than real-world implementation. Consequently, the findings should be viewed as dataset-specific proof-of-concept evidence, with further validation on independent, real-world, and multi-jurisdictional contract datasets required before practical adoption in operational decision-support systems.