用于高效且可解释的虾病文本分类的可解释多损失蒸馏框架
Explainable Multi-Loss Distillation Framework for Efficient and Interpretable Shrimp Disease Text Classification
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中文总结 AI 辅助
针对虾病图像分类仅能在疾病晚期检测的局限，提出SALT框架结合LIME、SHAP实现可解释性，在文本分类任务中兼顾性能、效率与可解释性，可用于虾病早期诊断。
中文摘要 AI 辅助
虾病分类已成为一个紧迫问题，因其对生产国，尤其是越南的进出口产出影响重大。现有多数研究聚焦于基于图像的分类，这类方法通常在疾病显现的晚期阶段运作，因此基于文本的分类具备实现早期及时疾病检测的潜力。为解决这一局限，我们提出SALT（基于多损失蒸馏的虾病文本分析）框架，该框架整合了使用局部可解释模型无关解释（LIME）和SHapley加性解释（SHAP）的可解释性分析，以评估模型预测结果并解释所学的语言特征。实验结果表明，SALT在多个蒸馏目标上取得了具有竞争力的性能，优于监督基线，同时在预测性能与计算效率之间实现了良好的权衡。此外，它展现出强大的可解释性，能准确识别与疾病描述相关的关键语言特征和语义模式。这些发现凸显了基于知识蒸馏的文本分类在未来虾病早期诊断及相关研究方向应用的潜力。
英文摘要
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.
发表机构
- FPT University（FPT大学）
- Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。
