Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification. The proposed algorithm, namely Time-CF, leverages shapelets and TimeGAN to provide counterfactual explanations for arbitrary time series classifiers. We validate the proposed method on several real-world univariate time series classification tasks from the UCR Time Series Archive. The results indicate that the counterfactual instances generated by Time-CF when compared to state-of-the-art methods, demonstrate better performance in terms of four explainability metrics: closeness, sensibility, plausibility, and sparsity.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationcounterfactualTime SeriesTime Series ClassificationSimilar Papers 제목 키워드 기반
Shapelet-Based Counterfactual Explanations for Multivariate Time Series
As machine learning and deep learning models have become highly prevalent in a multitude of domains, the main reservation in their adoption for decision-making processes is their black-box nature. The Explainable Artific…
counterfactualCounterfactual ExplanationDecision MakingExplainable artificial intelligence+5TriShGAN: Enhancing Sparsity and Robustness in Multivariate Time Series Counterfactuals Explanation
In decision-making processes, stakeholders often rely on counterfactual explanations, which provide suggestions about what should be changed in the queried instance to alter the outcome of an AI system. However, generati…
Metric LearningShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models
Explaining time series classification models is crucial, particularly in high-stakes applications such as healthcare and finance, where transparency and trust play a critical role. Although numerous time series classific…
Time Series ClassificationA Model-Agnostic SAT-based Approach for Symbolic Explanation Enumeration
In this paper titled A Model-Agnostic SAT-based approach for Symbolic Explanation Enumeration we propose a generic agnostic approach allowing to generate different and complementary types of symbolic explanations. More p…
image-classificationImage ClassificationInterpretable Counterfactual Explanations Guided by Prototypes
We propose a fast, model agnostic method for finding interpretable counterfactual explanations of classifier predictions by using class prototypes. We show that class prototypes, obtained using either an encoder or throu…
counterfactualDiagnostic