Resampling strategies for imbalanced regression: a survey and empirical analysis

George D. C. Cavalcanti; Juscimara G. Avelino; Rafael M. O. Cruz

REVIEW 3 major objections 5 minor 55 references

Resampling strategies for imbalanced regression: a survey and empirical analysis

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Resampling nearly always helps imbalanced regression, but the best strategy depends on the data, the model, and the metric.

desk verdict A useful, broad empirical benchmark on resampling for imbalanced regression, held back by one missing experimental parameter and some overclaimed dataset findings—revisable, not fatal. read the letter →

arxiv 2507.11902 v1 pith:3JRDDQP3 submitted 2025-07-16 cs.LG

Juscimara G. Avelino , George D. C. Cavalcanti , Rafael M. O. Cruz This is my paper · ORCID

classification cs.LG

keywords imbalancedregressionresamplingstrategiesrelevancefunctionSERAmetricutility-basedF1-scorerandomover-samplingGaussiannoiseWERCS

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Imbalanced regression treats problems where extreme or rare target values matter most, and machine-learning models tend to ignore them because they are underrepresented. The paper compares six resampling strategies on 30 datasets with six regression models, using two metrics designed to reward accuracy on rare values. It tries to establish that resampling is beneficial for the vast majority of regression models, with Gaussian noise, random over-sampling, and WERCS as the strongest strategies, and that the best choice depends on the dataset, the model, and the metric. If this holds, practitioners should not skip resampling but also should not expect a single universal winner.

What carries the argument

The load-bearing mechanism is the automatic relevance function $\varphi(y)$, built by pchip interpolation over control points derived from quartile-based boxplot statistics of the target variable. This function assigns each target value a relevance score between 0 and 1, and a threshold $t_R$ splits the data into rare and normal examples. The same function drives both halves of the study: each resampling strategy uses it to decide which cases to duplicate, remove, or synthesize, and both evaluation metrics (the utility-based F1-score and the SERA metric) compute scores from it. The whole comparison is therefore a comparison of strategies under one particular definition of which target values count as rare.

What would settle it

Re-run the 30-dataset comparison with a domain-defined relevance function, for example the NO2 concentration thresholds the paper cites, in place of the pchip/quartile-boxplot one; if the ranking of strategies changes substantially, the reported winners are artifacts of that automatic relevance definition rather than properties of imbalanced regression.

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Extended reading notes

Core claim

The paper's central claim is that resampling strategies improve imbalanced regression performance for the vast majority of models, and that the improvement is statistically significant when tested across multiple datasets. The best overall strategies are Gaussian noise, random over-sampling, and WERCS, while SmoteR, SMOGN, and random under-sampling are weaker in these experiments. No strategy wins everywhere: the winning combination shifts with the dataset and the learning algorithm, and the two evaluation metrics (utility-based F1-score and SERA) sometimes disagree. The paper also finds that the hardest problems are small datasets with few rare cases and high imbalance ratios, and that models perform better when the number of features is small.

Load-bearing premise

The whole comparison rests on the automatic relevance function correctly marking which target values are rare and important, because that same function decides both what each resampling strategy changes and what both metrics reward.

Editorial extensions

If this is right

Practitioners should not default to no resampling: for most regression models, applying Gaussian noise, random over-sampling, or WERCS improves imbalance-aware performance over the raw data.
There is no universal best strategy; resampling should be treated as a tunable component of the modeling pipeline, selected per dataset and per evaluation metric.
Strategies that inflate the training set, like random over-sampling (about 1421 percent growth), can be replaced by lighter ones like Gaussian noise and WERCS (about 1 to 3 percent growth) with comparable gains, saving training time.
Datasets that are small, have few rare cases, or have a high imbalance ratio are where resampling choices matter most, and default pipelines are most likely to fail there.
The two imbalance-aware evaluation lenses, F1-score and SERA, can disagree about which strategy wins, so reported performance should be metric-specific.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper: because the same automatic relevance function both selects rare cases for resampling and scores the results, the reported ranking is conditional on that choice of $\varphi$; adopting a domain-defined relevance function, such as regulatory thresholds, could reorder the winners.
Beyond the paper: the finding that dataset size and imbalance ratio drive difficulty suggests that a meta-learning approach recommending a resampling strategy from dataset characteristics could be feasible.
Beyond the paper: a direct test of the paper's conclusions would be to repeat the 30-dataset comparison with a different relevance threshold, for instance $t_R = 0.5$ or $0.9$, and check whether the Gaussian-noise, random-over-sampling, and WERCS advantage persists.
Beyond the paper: SERA's global, threshold-free evaluation combined with WERCS's threshold-free resampling suggests these two may be a particularly robust pairing when domain knowledge about the relevance threshold is absent.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: the paper's substance is a large, clean empirical comparison of six resampling strategies across six regressors and 30 datasets, with code and data online. The main findings—resampling mostly helps, no single winner, best strategy depends on model/metric/dataset—are not new; they were in Branco et al. and Moniz et al. What is new is the breadth and the public release. That is worth having.

The good stuff: the experimental protocol is sound. They use 2x10 CV, nested CV for resampling hyperparameters, report win counts, and back them with Friedman plus Nemenyi tests. The taxonomy in Section 2 is a reasonable organizing device, even if it overlaps with existing categorizations. The discussion of each strategy's behavior (Section 3.7) is genuinely useful. The GitHub release makes the benchmark checkable.

The soft spots, in order of severity. First, the F1-score results are not reproducible as reported. Eq. (10) introduces a weighting parameter p in the utility function, and the paper never states its value or whether it was tuned/fixed. RO is presented as a top strategy largely on F1 grounds, so this is a load-bearing omission. A reader cannot verify the F1 tables without guessing p. This is an easy fix—report p and, ideally, a sensitivity check—but until then that half of the evidence is unverifiable. Second, the claims that dataset characteristics 'significantly influence' results are based on visual inspection of box plots, not significance tests. As written, 'significantly' is doing work it shouldn't. Either soften the language or run a proper test (e.g., correlation or grouped comparison). Third, the 'first comprehensive empirical analysis' framing should be qualified against Branco et al. [10], which the paper already cites and which did empirical comparisons; the claim survives only if 'comprehensive' is defined carefully (e.g., including SERA and WERCS). Minor: Section 5 repeats a paragraph verbatim twice, and the SG failures on three large datasets (marked 'not completed') are included as zero values in the tables, which biases the win counts against SG; those runs should be reported as missing, not zero.

Overall: the central argument holds. The missing p is the only piece that undermines reproducibility, and it is fixable. This paper deserves a serious referee, not a desk reject. I'd send it out with a request to address p and the significance-language issue. Good candidate for a reading group too, but mainly as a reference benchmark.

Referee Report

3 major / 5 minor

Summary. The paper surveys and empirically evaluates six resampling strategies (SmoteR, Random Over-sampling, Random Under-sampling, Introduction of Gaussian Noise, SMOGN, and WERCS) for imbalanced regression, together with six regression models and two imbalanced-aware evaluation metrics (utility-based F1-score and SERA). It proposes a taxonomy of imbalanced regression approaches, reports win counts and Friedman/Nemenyi statistical analyses over 30 datasets, and derives practical lessons: resampling is usually beneficial, the best strategy depends on dataset, model, and metric, and dataset characteristics such as size and imbalance ratio affect performance. The authors provide code and data on GitHub.

Significance. If the findings hold, this is a useful and reasonably comprehensive benchmark for an under-studied problem, and the practical guidance (resample, but choose the strategy conditionally) is actionable. The paper's strengths include the multi-dataset experimental design, the use of appropriate non-parametric comparisons (Friedman and Nemenyi), the explicit treatment of both local and global metrics, and the public release of code and data. The main weakness is that one load-bearing experimental condition — the utility parameter p in Eq. (10) — is never reported, which makes the F1-score branch of the evidence non-reproducible. The dependence of the whole study on the pchip-based relevance function is a known limitation of the field and is acknowledged by the authors; I do not treat that as a flaw of this manuscript.

major comments (3)

[Section 4.3, Eq. (10), Tables 6 and 8] The utility parameter p is never specified. The F1-score results in Tables 6 and 8 and Figure 6 are computed through the utility function U^p_phi in Eq. (4), which depends on the weighted relevance phi_p in Eq. (10), but the manuscript does not state the value of p used, nor whether it was fixed or tuned. Since the headline ranking of RO as the best strategy under F1 rests on this branch of the evidence, the F1 results are not reproducible as written. Please report p (and the beta value in Eq. 16), and ideally include a sensitivity analysis over p to show whether the strategy rankings change.
[Section 5, RQ5, Figures 9-13] The claim that dataset size, number of rare cases, number of attributes, and imbalance ratio 'significantly influence' predictive performance is not supported by any statistical test reported in the paper; the evidence consists of visual inspection of line/scatter plots. Because RQ5 is one of the stated contributions, please either add appropriate statistical analyses (e.g., correlation or regression on per-dataset best F1 or on strategy ranks) or soften the wording to descriptive statements such as 'appear to be associated with' or 'show an association with'.
[Section 5, Tables 10-11 and Figures 6-7] The SMOGN strategy did not complete on the california, heat, and wine-quality datasets (Tables 8 and 9), but the Friedman and Nemenyi analyses do not state how missing values were handled. Please clarify whether the statistical tests and average ranks in Tables 10-11 use complete cases only or some imputation, since this affects interpretation of the SMOGN results and of the overall statistical comparisons.

minor comments (5)

[Abstract] There is a typo: 'wich uses metrics' should be 'which uses metrics'.
[Section 5, after Tables 6 and 7] The paragraph beginning 'By observing the score by rows...' is repeated verbatim twice; please remove the duplicate.
[Eq. (16)] The stated range '0 <= beta <= 1' is non-standard for the F-beta formula; F1-score normally corresponds to beta = 1. Please state explicitly which beta was used to compute the reported F1-scores, or simplify the equation to the balanced F1 form.
[Table 6] The XG row sums to 30.1 and the grand total to 180.1, presumably due to rounding of 1/n tie scores; please make the displayed totals consistent with 30 datasets and 180 dataset-model combinations.
[Section 5 and Appendix D] The text says that datasets with fewer features exhibit superior performance, but later states that a higher number of attributes leads to better model performance. Please reconcile these statements, since they appear contradictory without further explanation of the different analyses.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the headline claims are empirical rankings over 30 external datasets; the shared relevance function and the unreported utility parameter p are methodology and reproducibility concerns, not definitional reductions.

full rationale

This is an empirical benchmark study, not a derivation. The central claims — that resampling helps most regression models, that GN/RO/WERCS lead, and that the best strategy is context-dependent — are supported by external evidence: win counts over 30 standard datasets (Tables 6 and 7), per-dataset best/worst tables (Tables 8 and 9), and Friedman/Nemenyi significance tests (Figures 6 and 7). No equation in the paper constructs a reported outcome from its own input. Two structural couplings deserve scrutiny but are not circular. First, the relevance function phi is used both to define rare cases for every resampling strategy and to score both metrics (F1, Eqs. 14–16; SERA, Eq. 18); Section 2.1 explicitly states that 'using a different relevance function alters both the model evaluation and data resampling.' That is a stated modeling assumption shared with the field, and the outcome is not forced by it: WERCS, the strategy whose weights are most directly derived from phi (Algorithm 9, lines 3–7), wins only 10 times under F1 but 36 times under SERA, while RO wins 62 times under F1 but only 26 under SERA. This divergence between metrics shows the ranking is empirical, not definitional. Second, hyperparameters were tuned with SERA in nested cross-validation (Section 4.1) before F1 was reported; this couples the two metrics but does not define the results. The never-stated utility weight p in Eq. (10) is a genuine reproducibility gap (the skeptic's point), and the attribute-count claims in Section 5 ('datasets with fewer features exhibit superior performance') contradict Appendix D ('a higher number leads to better model performance'); both are correctness risks, not circularity. The only self-citation involving the authors, reference [20] (Roy, Cruz, Sabourin, Cavalcanti), appears within a background list of eight binary-classification empirical studies in the Introduction and is not load-bearing. A score of 1 reflects that minor self-citation and the shared-phi coupling; the core experimental claims are self-contained against external benchmarks.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical or formal entities; the taxonomy is a reorganization of existing categories and the relevance function is from Ribeiro's earlier work. The load-bearing assumptions are the relevance function, the utility-based F1 parameter p, and the representativeness of the dataset corpus.

free parameters (3)

relevance threshold tR = 0.8
User-defined threshold dividing rare from normal target values, adopted from prior work [5,10,36]. It determines all resampling outcomes and the local F1 evaluation.
utility weighting parameter p = not reported
Appears in Eq. 10 for weighted relevance in the utility function used by Precision/Recall/F1; no value is given in the experimental section, so exact F1 results are not reproducible from the text alone.
resampling hyperparameters (u, o, k, delta) = tuned per dataset by inner 2-fold CV on SERA; values not listed
Over/under-sampling rates, k, and Gaussian perturbation amplitude are optimized on SERA and materially change the comparisons.

assumptions (4)

domain assumption The pchip relevance function with Tukey boxplot control points correctly captures the user-relevance of target values.
Section 2.1 defines rare cases and every strategy and evaluation metric depends on phi; if phi is wrong for a domain, the ranking of strategies is about the wrong quantities.
domain assumption F1-score for regression and SERA are valid measures of imbalanced-regression performance.
Section 4.3 chooses these metrics as ground truth; the utility-based F1 adds an unreported parameter p, making the assumption stronger than stated.
domain assumption The 30 benchmark datasets are representative of imbalanced regression problems.
Section 4.1 selects datasets to match the frequency generally used in studies; no formal argument connects this corpus to real-world imbalanced regression.
standard math Friedman and Nemenyi tests are applied correctly with the 30 datasets as independent blocks.
Section 5 uses these tests for multiple comparisons over 30 datasets; standard assumptions of paired comparisons across datasets are implicitly invoked.

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Cite this review

Pith. "Pith review of Resampling strategies for imbalanced regression: a survey and empirical analysis." pith.science (2026). https://pith.science/paper/3JRDDQP3

@misc{pith2026250711902,
  author       = {Pith},
  title        = {Pith review of: Resampling strategies for imbalanced regression: a survey and empirical analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3JRDDQP3}},
  note         = {Machine review of arXiv:2507.11902}
}

read the original abstract

Imbalanced problems can arise in different real-world situations, and to address this, certain strategies in the form of resampling or balancing algorithms are proposed. This issue has largely been studied in the context of classification, and yet, the same problem features in regression tasks, where target values are continuous. This work presents an extensive experimental study comprising various balancing and predictive models, and wich uses metrics to capture important elements for the user and to evaluate the predictive model in an imbalanced regression data context. It also proposes a taxonomy for imbalanced regression approaches based on three crucial criteria: regression model, learning process, and evaluation metrics. The study offers new insights into the use of such strategies, highlighting the advantages they bring to each model's learning process, and indicating directions for further studies. The code, data and further information related to the experiments performed herein can be found on GitHub: https://github.com/JusciAvelino/imbalancedRegression.

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