TSEvo: Evolutionary Counterfactual Explanations for Time Series Classification

Höllig, Jacqueline; Kulbach, Cedric; Thoma, Steffen

doi:10.1109/icmla55696.2022.00013

Cited by 5 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…released by the authors. • TSEvo, proposed by [31], is a recent method that uses genetic algorithms to solve an optimization problem in which plausibility, proximity, and sparsity are enforced. It also searches for contiguity in the counterfactuals, although it is not a term in the optimization objective function, but a mutation strategy instead.…”

Section: Baseline Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Sub-SpaCE: Subsequence-based Sparse Counterfactual Explanations for Time Series Classification Problems

Refoyo,

Luengo

2023

Preprint

View full text Add to dashboard Cite

The interpretation of existing machine learning models has become a critical task to facilitate the widespread adoption of AI across different domains, leading to the apparition of eXplainable AI (XAI). However, there exists an imbalance between methods tailored for specific data domains, with dedicated approaches for time series data having received limited attention until now. Moreover, current approaches often overlook the unique challenges present in time series data. In this paper, we introduce Subsequence-based Sparse Counterfactual Explanations (Sub-SpaCE), a novel method tailored for time series classification problems. Sub-SpaCE employs genetic algorithms with customized mutation and initialization processes, promoting changes in a small number of subsequences to generate highly sparse and plausible counterfactual explanations. Our empirical evaluations on various datasets demonstrate Sub-SpaCE's superior performance, achieving an optimal balance between sparsity and plausibility in counterfactual explanations for time series data.

show abstract

Section: Baseline Methodsmentioning

confidence: 99%

“…More recently, Höllig et al proposed TSEvo [31], a model-agnostic method for multivariate time series counterfactual explanations. They use a multi-objective genetic algorithm to solve an optimization problem where proximity, sparsity, and plausibility are enforced.…”

Section: Counterfactual Explanations For Time Series Datamentioning

confidence: 99%

Sub-SpaCE: Subsequence-based Sparse Counterfactual Explanations for Time Series Classification Problems

Refoyo,

Luengo

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The time component impedes the usage of existing methods (Ismail et al, 2020). Thus, increasing effort is put into adapting existing methods to time series (e.g., LEFTIST based on SHAP/Lime (Guillemé et al, 2019), Temporal Saliency Rescaling for Saliency Methods (Ismail et al, 2020), or Counterfactuals (Ates et al, 2021;Delaney et al, 2021;Höllig et al, 2022)). Compared to images or textual data, humans cannot intuitively and instinctively understand the underlying information in time series data.…”

Section: Statement Of Needmentioning

confidence: 99%

TSInterpret: A Python Package for the Interpretability of Time Series Classification

Höllig¹,

Kulbach²,

Thoma³

2023

JOSS

Self Cite

View full text Add to dashboard Cite

TSInterpret is a python package that enables post-hoc interpretability and explanation of black-box time series classifiers with three lines of code. Due to the specific structure of time series (i.e., non-independent features (Ismail et al., 2020)) and unintuitive visualizations (Siddiqui et al., 2019), traditional interpretability and explainability libraries like Captum (Kokhlikyan et al., 2020), Alibi Explain (Klaise et al., 2021, or tf-explain (Meudec, 2021) find limited usage. TSInterpret specifically addresses the issue of black-box time series classification by providing a unified interface to state-of-the-art interpretation algorithms in combination with default plots. In addition, the package provides a framework for developing additional easy-to-use interpretability methods.

show abstract

Sub-SpaCE: Subsequence-Based Sparse Counterfactual Explanations for Time Series Classification Problems

Refoyo,

Luengo

2024

Communications in Computer and Information Science

View full text Add to dashboard Cite

TSEvo: Evolutionary Counterfactual Explanations for Time Series Classification

Cited by 5 publications

References 26 publications

Sub-SpaCE: Subsequence-based Sparse Counterfactual Explanations for Time Series Classification Problems

Sub-SpaCE: Subsequence-based Sparse Counterfactual Explanations for Time Series Classification Problems

TSInterpret: A Python Package for the Interpretability of Time Series Classification

Sub-SpaCE: Subsequence-Based Sparse Counterfactual Explanations for Time Series Classification Problems

Contact Info

Product

Resources

About