[Research on the classification method of unbalanced data for the assessment of traffic accident disability level].

内容摘要

The evaluation of disability grades in traffic accidents is a professional forensic clinical appraisal matter, and its results directly affect the fairness of judicial compensation. In the construction of automated disability grade evaluation models, the imbalanced distribution of disability cases leads to low recognition accuracy for minority categories, becoming a key bottleneck restricting the technology's implementation. In response, this paper proposes an imbalanced data classification method based on a hybrid parameter scaling weight optimization mechanism. First, a loss weight calculation model is constructed based on category proportion, category sparsity, and category diversity. Second, the loss weight calculation model is designed by integrating the focal loss function's ability to focus on hard samples with the cross-entropy loss function's global gradient stability advantage. Then, at the early stages of training, the model proposed in this paper aligns sensitivity to imbalanced categories and constructs a low-computational-demand hybrid parameter scaling weight optimization mechanism. Experimental results show that, compared with the best-performing baseline methods, the proposed method significantly improves both accuracy and macro-F1 score on the traffic accident disability grade dataset. It can effectively enhance the classification performance of minority grade categories in imbalanced data and help improve the accuracy of automated appraisal in judicial identification of traffic accident disability grades. 交通事故伤残等级评定是专业的法医临床鉴定事项，其结果直接影响司法赔偿的公平性。在自动化伤残等级评定模型构建中，不平衡的伤残案例分布使模型对少数类别的识别准确率较低，成为制约技术落地的关键瓶颈。对此，本文提出一种基于混合参数缩放权重优化机制的不平衡数据分类方法。首先，基于类别比例、类别稀疏度和类别多样性构建损失权重计算模型；其次，融合焦点损失函数对难分类样本的聚焦能力与交叉熵损失函数的全局梯度稳定性，设计损失权重计算模型；然后，在训练初期对齐本文模型对不平衡类别的敏感度，构建低算力需求的混合参数缩放权重优化机制。实验结果表明，相较于性能最优的基线方法，本文所提方法在交通事故伤残等级数据集上的准确率与宏F1值均有较大幅度提升，能有效改善不平衡数据中少数类等级的分类性能，有助于提高交通事故伤残等级司法鉴定中自动化鉴定的准确率。.