用多损失蒸馏提升虾病文本分类的准确率与可解释性
Explainable Multi-Loss Distillation Framework for Efficient and Interpretable Shrimp Disease Text Classification
- 采用多损失蒸馏框架，融合LIME和SHAP实现预测可解释性
- 在多个指标上优于监督基线，兼顾性能与计算效率
- 能精准识别疾病描述中的关键语义特征，适合农业诊断场景
虾病分类因对越南等生产国进出口贸易的重大影响而变得尤为紧迫。现有研究多聚焦于疾病晚期的图像分类，而文本分类具备早期预警潜力。为此，本文提出SALT（Shrimp disease text Analysis with multi-Loss disTillation）框架，结合局部可解释模型无关解释（LIME）和加性可解释性（SHAP）进行可解释性分析，评估模型预测并解析学习到的语言特征。实验表明，SALT在多个蒸馏目标下表现优异，超越监督基线，在预测性能与计算效率间取得良好平衡。同时具备强可解释性，能准确识别与疾病描述相关的关键词和语义模式。研究结果凸显基于知识蒸馏的文本分类在早期虾病诊断中的应用前景及未来研究方向。
原文摘要 · Abstract (English)
Shrimp disease classification has become an urgent issue due to its significant impact on the import-export output of producing countries, particularly Vietnam. Most existing studies focus on image-based classification, which typically operates at the late stage of disease manifestation. Therefore, text-based classification has the potential to enable early and timely disease detection. To address this limitation, we introduce the SALT (Shrimp disease text Analysis with multi-Loss disTillation) framework, which incorporates explainability analysis using Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to evaluate model predictions and interpret the learned linguistic features. Experimental results demonstrate that SALT achieves competitive performance across multiple distillation objectives, outperforming supervised baselines while providing a favorable trade-off between predictive performance and computational efficiency. Moreover, it exhibits strong explainability, accurately identifying key linguistic features and semantic patterns relevant to disease descriptions. These findings highlight the potential of knowledge distillation-based text classification for future applications in early shrimp disease diagnosis and related research directions.
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