Authors:
Qi Huang
;
Sofoklis Kitharidis
;
Thomas Bäck
and
Niki van Stein
Affiliation:
Institute of Advanced Computer Science, Leiden University, Einsteinweg 55, Leiden, The Netherlands
Keyword(s):
Explainable Artificial Intelligence, Counterfactuals, Time-Series Classification, Evolutionary Computation.
Abstract:
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 predefine
d 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.
(More)