TX-Gen：时间序列分类稀疏反事实解释的多目标优化
TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification
AI总结：
TX-Gen提出基于NSGA-II的进化多目标优化算法，生成稀疏、有效且与原始序列差异最小的反事实解释，提升时间序列模型的透明度和可解释性。
摘要：
在时间序列分类中，理解模型决策对于其在医疗保健和金融等高风险领域的应用至关重要。反事实解释通过呈现改变模型预测的替代输入来提供见解，是一种有前景的解决方案。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。在本文中，我们介绍了TX-Gen，一种基于非支配排序遗传算法II（NSGA-II）的生成反事实解释的新算法。TX-Gen利用进化多目标优化来寻找一组既稀疏又有效的多样化反事实，同时保持与原始时间序列的最小不相似性。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和可解释性。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。
英文摘要：
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.
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。
