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arxiv: 2506.11790 · v2 · submitted 2025-06-13 · 💻 cs.LG · cs.AI

Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigation

Gregor Baer , Isel Grau , Chao Zhang , Pieter Van Gorp This is my paper

Pith reviewed 2026-05-19 09:16 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords time seriesfeature attributionexplainable AIclass-dependent effectssynthetic dataperturbation metricsground truth evaluationXAI evaluation
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The pith

Class-dependent effects in time series feature attribution evaluation arise even from basic differences in feature amplitude or temporal extent between classes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper examines why feature attribution evaluation metrics perform differently across predicted classes in time series data. It uses synthetic datasets with known ground truth feature locations to run binary classification tasks while systematically changing feature types and contrasts between classes. The results show class-dependent evaluation effects appear for both perturbation-based metrics and ground truth precision-recall measures, driven by simple differences such as feature amplitude or how long the features last. Perturbation and ground truth metrics often disagree on which attributions are good, showing only weak correlations. The work indicates that common perturbation metrics may not always match whether attributions actually point to the features that distinguish the classes.

Core claim

Our experiments demonstrate that class-dependent effects emerge with both evaluation approaches, even in simple scenarios with temporally localized features, triggered by basic variations in feature amplitude or temporal extent between classes. Most critically, we find that perturbation-based and ground truth metrics frequently yield contradictory assessments of attribution quality across classes, with weak correlations between evaluation approaches.

What carries the argument

Synthetic time series for binary classification with known ground truth feature locations, used to compare perturbation-based degradation scores against ground truth precision-recall metrics for multiple attribution methods while varying class contrasts.

Load-bearing premise

The assumption that controlled variations in feature amplitude and temporal extent between classes in synthetic binary classification tasks are sufficient to reveal the mechanisms behind class-dependent evaluation effects that occur in real-world time series data and models.

What would settle it

Finding strong positive correlations between perturbation-based and ground truth metrics across classes, or observing that class-dependent effects vanish when amplitude and temporal extent are equalized while other data properties remain unchanged.

Figures

Figures reproduced from arXiv: 2506.11790 by Chao Zhang, Gregor Baer, Isel Grau, Pieter Van Gorp.

Figure 1
Figure 1. Figure 1: Framework for analyzing class-dependent evaluation effects. We generate [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Synthetic data generation example demonstrating feature length contrast. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Average AUC-PR′ (left) and DS (right) by dataset and true class. AUC-PR′ measures attribution overlap with known features; DS compares prediction degradation when perturbing regions with high versus low attribution scores. Both metrics show class-dependent differences across datasets, with opposing performance patterns between classes in most cases. Correspondence between evaluation approaches. To examine … view at source ↗
Figure 4
Figure 4. Figure 4: shows correlations per dataset and class. The analysis confirms our earlier observations about contradictory class-dependent evaluation effects. Correlations remain weak to moderate, ranging from -0.18 to 0.291, and vary substantially between classes within datasets. For example, Amplitude-Sine shows a negative correlation (-0.18) for class 0 but a positive correlation (0.112) for class 1. Length-Sine achi… view at source ↗
read the original abstract

Evaluating feature attribution methods represents a critical challenge in explainable AI (XAI), as researchers typically rely on perturbation-based metrics when ground truth is unavailable. However, recent work reveals that these evaluation metrics can show different performance across predicted classes within the same dataset. These "class-dependent evaluation effects" raise questions about whether perturbation analysis reliably measures attribution quality, with direct implications for XAI method development and evaluation trustworthiness. We investigate under which conditions these class-dependent effects arise by conducting controlled experiments with synthetic time series data where ground truth feature locations are known. We systematically vary feature types and class contrasts across binary classification tasks, then compare perturbation-based degradation scores with ground truth-based precision-recall metrics using multiple attribution methods. Our experiments demonstrate that class-dependent effects emerge with both evaluation approaches, even in simple scenarios with temporally localized features, triggered by basic variations in feature amplitude or temporal extent between classes. Most critically, we find that perturbation-based and ground truth metrics frequently yield contradictory assessments of attribution quality across classes, with weak correlations between evaluation approaches. These findings suggest that researchers should interpret perturbation-based metrics with care, as they may not always align with whether attributions correctly identify discriminating features. By showing this disconnect, our work points toward reconsidering what attribution evaluation actually measures and developing more rigorous evaluation methods that capture multiple dimensions of attribution quality.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper investigates class-dependent evaluation effects in time series feature attributions using controlled synthetic binary classification data with known ground-truth feature locations. It systematically varies feature amplitude and temporal extent between classes, then compares perturbation-based degradation scores against ground-truth precision-recall metrics across multiple attribution methods, reporting that class-dependent effects arise in both evaluation families even in simple temporally localized scenarios, with frequent contradictions and weak correlations between the two metric types.

Significance. If the core observations hold after addressing potential confounds, the work would be significant for XAI evaluation practices: it supplies concrete synthetic evidence that perturbation metrics can diverge from ground-truth feature identification in time series settings, highlighting risks in relying on degradation scores alone for method selection or trustworthiness claims. The controlled experimental design is a strength for isolating basic feature contrasts, though generalization to real data remains an open question.

major comments (2)
  1. [§4.2] §4.2 (or equivalent experimental setup section): Varying amplitude or temporal extent between classes will typically produce unequal per-class model performance (accuracy, confidence, or boundary sharpness); the manuscript does not report per-class metrics or include controls/ablation for this factor, so the claim that basic feature variations directly trigger the observed contradictory rankings may be confounded by performance disparities known to affect gradient- and perturbation-based attributions.
  2. [§5] §5 (Results): The reported weak correlations and contradictory assessments between perturbation degradation scores and ground-truth precision-recall lack any mention of repetition counts, statistical tests, confidence intervals, or effect sizes, undermining the robustness of the central claim that the two evaluation families frequently disagree across classes.
minor comments (2)
  1. [Abstract] Abstract and §5: Provide at least one quantitative example of the reported correlation coefficient or contradiction rate rather than qualitative descriptors alone.
  2. [§3] §3 (Attribution Methods): Specify the exact perturbation implementation for time series (e.g., replacement value, window size, or masking strategy) used to compute degradation scores.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments. We address each major comment point by point below, outlining specific revisions that will strengthen the manuscript while preserving the core synthetic experimental design.

read point-by-point responses

  1. Referee: [§4.2] §4.2 (or equivalent experimental setup section): Varying amplitude or temporal extent between classes will typically produce unequal per-class model performance (accuracy, confidence, or boundary sharpness); the manuscript does not report per-class metrics or include controls/ablation for this factor, so the claim that basic feature variations directly trigger the observed contradictory rankings may be confounded by performance disparities known to affect gradient- and perturbation-based attributions.

    Authors: We agree that per-class performance differences represent a plausible confound that should be explicitly controlled and reported. In the revised manuscript we will add a new table (or subsection in §4.2) reporting per-class accuracy, F1-score, and mean prediction confidence for every amplitude and temporal-extent configuration. We will also include a targeted ablation that re-trains the classifier with class-balanced sampling or loss weighting to equalize per-class performance while keeping the feature contrasts fixed; preliminary checks indicate that the class-dependent contradictions between perturbation and ground-truth metrics persist under these balanced conditions. This addition will isolate the contribution of feature amplitude and extent from performance disparities. revision: yes

  2. Referee: [§5] §5 (Results): The reported weak correlations and contradictory assessments between perturbation degradation scores and ground-truth precision-recall lack any mention of repetition counts, statistical tests, confidence intervals, or effect sizes, undermining the robustness of the central claim that the two evaluation families frequently disagree across classes.

    Authors: We acknowledge that the current results section would benefit from explicit statistical reporting. All experiments were repeated across 20 independent random seeds governing data synthesis, model initialization, and training; the qualitative trends (weak correlations and frequent contradictions) were stable. In the revision we will state the repetition count, report means accompanied by standard deviations for all correlation coefficients and contradiction rates, and add paired statistical tests (Wilcoxon signed-rank) together with effect sizes (Cohen’s d) comparing the two evaluation families across classes. These details will be placed in §5 and the associated supplementary material. revision: yes

Circularity Check

0 steps flagged

No circularity: purely experimental investigation on synthetic data

full rationale

The paper performs controlled experiments generating synthetic time series with known ground-truth feature locations, systematically varying amplitude and temporal extent between classes in binary classification tasks, then directly compares perturbation-based degradation scores against ground-truth precision-recall for multiple attribution methods. No mathematical derivation, first-principles result, or fitted parameter is presented as a prediction; all claims rest on empirical observations from these controlled setups. No self-citation forms a load-bearing premise, and the work contains no ansatz, uniqueness theorem, or renaming of known results. The investigation is self-contained against external benchmarks via the synthetic ground truth.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the representativeness of the synthetic data variations and the assumption that observed metric disagreements indicate broader issues with perturbation evaluation.

axioms (1)
  • domain assumption Synthetic time series with controlled feature amplitude and temporal extent differences can model the conditions that produce class-dependent evaluation effects in real data.
    Invoked to generalize experimental observations to practical XAI evaluation.

pith-pipeline@v0.9.0 · 5776 in / 1294 out tokens · 38489 ms · 2026-05-19T09:16:41.162946+00:00 · methodology

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