TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification
<|reference_start|>TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification: 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.<|reference_end|>
@article{huang2024tx-gen:,
    title={TX-Gen: Multi-Objective Optimization for Sparse Counterfactual
  Explanations for Time-Series Classification},
    author={Qi Huang, Sofoklis Kitharidis, Thomas B"ack, Niki van Stein},
    journal={arXiv preprint arXiv:2409.09461},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09461},
    primaryClass={cs.LG cs.AI cs.NE}
}