Conceitos Básicos
There is no single best counterfactual interpretability method for deep learning time series classification, as different methods excel in different metrics and are influenced by the choice of classifier.
Estatísticas
The study uses 20 univariate datasets from the UCR archive and 10 multivariate datasets from the UEA archive.
Six counterfactual methods were evaluated: NUN CF, NG, COMTE, SETS, wCF, and TSEvo.
Three deep learning classifiers were used: FCN, MLP, and InceptionTime.
A threshold of 0.25% of the original instance range was used for the ThreshL0 sparsity metric.
A 1% time length tolerance was applied for the NumSeg segment sparsity metric.
The neighborhood size k for plausibility metrics (Distall and Distclass) was set to 5.