论文标题
解释生物医学文本分类的黑盒模型
Explaining Black-box Models for Biomedical Text Classification
论文作者
论文摘要
在本文中，我们提出了一种名为生物医学自信物品的说明（BioCie）的新颖方法，旨在旨在事后解释生物医学文本分类的黑盒机器学习模型。使用域知识的来源和自信的项目集挖掘方法，BioCie将黑框的决策空间离散为较小的子空间，并提取不同子空间中输入文本和类标签之间的语义关系。自信的项目集发现了生物医学概念与Black-Box决策空间中的类标签的关系。 Biocie使用这些项目集近似黑盒的行为以进行单个预测。优化忠诚度，可解释性和覆盖范围，生物友会产生代表黑盒决策边界的班级解释。对各种生物医学文本分类任务和黑框模型的评估结果表明，生物群体可以在产生简洁，准确和可解释的解释方面超越基于扰动的决策和决策集。 Biocie分别将实例和班级解释的忠诚提高了11.6％和7.5％。它还提高了解释性的解释性8％。 Biocie可有效地用来解释黑盒生物医学文本分类模型如何将输入文本与类标签联系起来。源代码和补充材料可在https://github.com/mmoradi-iut/biocie上获得。
In this paper, we propose a novel method named Biomedical Confident Itemsets Explanation (BioCIE), aiming at post-hoc explanation of black-box machine learning models for biomedical text classification. Using sources of domain knowledge and a confident itemset mining method, BioCIE discretizes the decision space of a black-box into smaller subspaces and extracts semantic relationships between the input text and class labels in different subspaces. Confident itemsets discover how biomedical concepts are related to class labels in the black-box's decision space. BioCIE uses the itemsets to approximate the black-box's behavior for individual predictions. Optimizing fidelity, interpretability, and coverage measures, BioCIE produces class-wise explanations that represent decision boundaries of the black-box. Results of evaluations on various biomedical text classification tasks and black-box models demonstrated that BioCIE can outperform perturbation-based and decision set methods in terms of producing concise, accurate, and interpretable explanations. BioCIE improved the fidelity of instance-wise and class-wise explanations by 11.6% and 7.5%, respectively. It also improved the interpretability of explanations by 8%. BioCIE can be effectively used to explain how a black-box biomedical text classification model semantically relates input texts to class labels. The source code and supplementary material are available at https://github.com/mmoradi-iut/BioCIE.
