ConceptCF generates time-series counterfactuals by evolving interpretable concepts (scale, bias, frequency bands) with a genetic algorithm, achieving top-tier or near-top-tier scores on validity, proximity, sparsity, and plausibility across five datasets.
Title resolution pending
1 Pith paper cite this work, alongside 14 external citations. Polarity classification is still indexing.
1
Pith paper citing it
14
external citations · OpenAlex
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
ConceptCF: Concept-based Counterfactuals for the Explainability of Time Series
ConceptCF generates time-series counterfactuals by evolving interpretable concepts (scale, bias, frequency bands) with a genetic algorithm, achieving top-tier or near-top-tier scores on validity, proximity, sparsity, and plausibility across five datasets.