考虑因果约束的异常对象反事实解释
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国家自然科学基金面上项目(62376001); 安徽省自然科学基金面上项目(2308085MF215)


Counterfactual Explanation of Anomalous Objects Considering Causal Constraints
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    摘要:

    现有的异常检测方法大多关注算法的效率和精确度等, 而忽视了异常对象的可解释性. 反事实解释方法是当前可解释机器学习的研究热点之一, 旨在通过对研究对象的特征进行扰动, 进而生成反事实示例以解释模型的决策结果. 在实际应用中, 特征之间可能存在某种因果关系. 然而, 现有基于反事实的可解释方法大多关注如何生成更多样的反事实示例, 却忽视了特征之间的因果关系, 导致可能产生不合理的反事实解释. 为此, 提出了一种考虑因果约束的异常对象反事实解释算法IARC. 该方法在生成反事实解释时, 通过将特征间的因果性纳入目标函数来衡量该次扰动是否可行, 并通过改进后的遗传算法进行求解, 从而生成合理的反事实解释. 此外, 提出了一种新的度量指标, 用于衡量所生成反事实解释的矛盾程度. 同多个先进反事实解释方法在多个真实数据集上进行了对比实验和详细的案例可解释分析. 实验结果表明, 所提出的方法能够为异常对象生成具有强合理性的反事实解释.

    Abstract:

    Most existing anomaly detection methods focus on algorithm efficiency and accuracy while overlooking the interpretability of detected anomalous objects. Counterfactual explanation, a research hot spot in interpretable machine learning, aims to explain model decisions by perturbing the features of the instances under study and generating counterfactual examples. In practical applications, there may be causal relationships among features. However, most existing counterfactual-based interpretability methods concentrate on how to generate more diverse counterfactual examples, overlooking the causal relationships among features. Consequently, unreasonable counterfactual explanations may be produced. To address this issue, this study proposes an algorithm to interpret anomaly via reasonable counterfactuals (IARC) that consider causal constraints. In the process of generating counterfactual explanations, the proposed method incorporates the causality between features into the objective function to evaluate the feasibility of each perturbation, and employs an improved genetic algorithm for solution optimization, thereby generating rational counterfactual explanations. Additionally, a novel measurement metric is introduced to quantify the degree of contradiction in the generated counterfactual explanations. Comparative experiments and detailed case studies are conducted on multiple real-world datasets, benchmarking the proposed method against several state-of-the-art methods. The experimental results demonstrate that the proposed method can generate highly rational counterfactual explanations for anomalous objects.

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童启辉,周鹏,张燕平.考虑因果约束的异常对象反事实解释.计算机系统应用,2024,33(10):140-151

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  • 收稿日期:2024-04-03
  • 最后修改日期:2024-04-29
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  • 在线发布日期: 2024-08-21
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