In time-series classification, understanding model decisions is crucial for their application in high-stakes domains such as healthcare and finance. Counterfactual explanations, which provide insights by presenting alternative inputs that change model predictions, offer a promising solution. However, existing methods for generating counterfactual explanations for time-series data often struggle with balancing key objectives like proximity, sparsity, and validity. In this paper, we introduce TX-Gen, a novel algorithm for generating counterfactual explanations based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). TX-Gen leverages evolutionary multi-objective optimization to find a diverse set of counterfactuals that are both sparse and valid, while maintaining minimal dissimilarity to the original time series. By incorporating a flexible reference-guided mechanism, our method improves the plausibility and interpretability of the counterfactuals without relying on predefined assumptions. Extensive experiments on benchmark datasets demonstrate that TX-Gen outperforms existing methods in generating high-quality counterfactuals, making time-series models more transparent and interpretable.

翻译：在时间序列分类中，理解模型决策对其在医疗和金融等高风险领域的应用至关重要。反事实解释通过提供能够改变模型预测的替代输入来提供洞见，是一种颇具前景的解决方案。然而，现有的时间序列数据反事实解释生成方法往往难以平衡邻近性、稀疏性和有效性等关键目标。本文提出TX-Gen，一种基于非支配排序遗传算法II（NSGA-II）的新型反事实解释生成算法。TX-Gen利用进化多目标优化来寻找一组多样化、既稀疏又有效的反事实，同时保持与原始时间序列的最小差异。通过引入灵活的参考引导机制，我们的方法在不依赖预定义假设的情况下，提高了反事实的合理性与可解释性。在基准数据集上的大量实验表明，TX-Gen在生成高质量反事实方面优于现有方法，使时间序列模型更加透明和可解释。