Info-CELS：信息性显著性图引导的反事实解释
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation
AI总结：
针对 CELS 反事实解释模型有效性不足的问题，本文提出移除掩码归一化的 Info-CELS，在多领域数据集上提升了反事实解释的有效性与信息量。
摘要：
随着对可解释机器学习方法的需求持续增长，越来越需要人类参与，为模型决策提供信息丰富的解释。这对于在基于 AI 的系统中建立信任和透明度是必要的，并促成了可解释人工智能（XAI）领域的出现。最近，一种名为 CELS 的新型反事实解释模型被提出。CELS 为关注的实例学习显著性图，并在所学显著性图的引导下生成反事实解释。尽管 CELS 代表了利用所学显著性图的首次尝试，不仅用于直观解释时间序列分类器作出决策背后的原因，还用于探索事后反事实解释，但它存在局限：为了确保高邻近性和稀疏性，其有效性较低。本文提出一种建立在 CELS 之上的增强方法。虽然原始模型在稀疏性和邻近性方面取得了有前景的结果，但它在有效性方面面临局限。我们提出的方法通过移除掩码归一化来解决这一局限，从而提供更具信息性且更有效的反事实解释。通过在来自多个领域的数据集上进行大量实验，我们证明该方法优于 CELS 模型，实现了更高的有效性，并产生更具信息性的解释。
英文摘要：
As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in AI-based systems, leading to the emergence of the Explainable Artificial Intelligence (XAI) field. Recently, a novel counterfactual explanation model, CELS, has been introduced. CELS learns a saliency map for the interest of an instance and generates a counterfactual explanation guided by the learned saliency map. While CELS represents the first attempt to exploit learned saliency maps not only to provide intuitive explanations for the reason behind the decision made by the time series classifier but also to explore post hoc counterfactual explanations, it exhibits limitations in terms of high validity for the sake of ensuring high proximity and sparsity. In this paper, we present an enhanced approach that builds upon CELS. While the original model achieved promising results in terms of sparsity and proximity, it faced limitations in validity. Our proposed method addresses this limitation by removing mask normalization to provide more informative and valid counterfactual explanations. Through extensive experimentation on datasets from various domains, we demonstrate that our approach outperforms the CELS model, achieving higher validity and producing more informative explanations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
