多目标子序列基于稀疏反事实解释法用于多变量时间序列分类
Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification
AI总结：
Multi-SpaCE是一种用于多变量时间序列分类的多目标反事实解释方法，通过NSGA-II算法平衡多个目标，确保有效性并提供灵活的解决方案。
摘要：
深度学习系统在复杂任务中表现出色，但往往缺乏透明性，限制了其在关键应用中的使用。反事实解释，作为可解释人工智能（XAI）的核心工具，通过识别对输入进行最小修改以改变预测结果来提供对模型决策的洞察。然而，现有时间序列数据的方法受限于单变量假设、对修改的刚性约束或缺乏有效性保证。本文介绍了Multi-SpaCE，一种用于多变量时间序列的多目标反事实解释方法。使用非支配排序遗传算法II（NSGA-II），Multi-SpaCE平衡了接近性、稀疏性、合理性和连续性。与大多数方法不同，它确保了完美有效性，支持多变量数据并提供了解决方案的帕累托前沿，使不同终端用户需求具有灵活性。在多样化的数据集上的全面实验展示了Multi-SpaCE能够一致地实现完美有效性和比现有方法更优越的性能。
英文摘要：
Deep Learning systems excel in complex tasks but often lack transparency, limiting their use in critical applications. Counterfactual explanations, a core tool within eXplainable Artificial Intelligence (XAI), offer insights into model decisions by identifying minimal changes to an input to alter its predicted outcome. However, existing methods for time series data are limited by univariate assumptions, rigid constraints on modifications, or lack of validity guarantees. This paper introduces Multi-SpaCE, a multi-objective counterfactual explanation method for multivariate time series. Using non-dominated ranking genetic algorithm II (NSGA-II), Multi-SpaCE balances proximity, sparsity, plausibility, and contiguity. Unlike most methods, it ensures perfect validity, supports multivariate data and provides a Pareto front of solutions, enabling flexibility to different end-user needs. Comprehensive experiments in diverse datasets demonstrate the ability of Multi-SpaCE to consistently achieve perfect validity and deliver superior performance compared to existing methods.
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。
