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.

翻译：随着对可解释机器学习方法的需求持续增长，人类参与为模型决策提供信息性解释的必要性日益凸显。这对于在基于人工智能的系统中建立信任和透明度至关重要，并由此催生了可解释人工智能（XAI）领域。最近，一种新颖的反事实解释模型CELS被提出。CELS为特定实例学习一个显著图，并基于学习到的显著图生成反事实解释。尽管CELS首次尝试利用学习到的显著图，不仅为时间序列分类器所作决策背后的原因提供直观解释，还探索事后反事实解释，但它在确保高邻近性和稀疏性的同时，在有效性方面存在局限。本文提出了一种在CELS基础上改进的方法。原始模型在稀疏性和邻近性方面取得了有希望的结果，但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，以提供更具信息性和有效性的反事实解释。通过在多个领域的数据集上进行广泛实验，我们证明我们的方法优于CELS模型，实现了更高的有效性并生成了更具信息性的解释。