Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class

Annie D'souza; Sunita Sarawagi; Swetha M

REVIEW 4 major objections 5 minor 30 references

Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Splitting majority into clear and overlap classes improves synthetic minority data.

desk verdict A genuinely simple and mostly convincing fix for imbalanced tabular generation, but the missing random-third-class control leaves the mechanism underdetermined. read the letter →

arxiv 2412.15657 v2 pith:VRONQK4J submitted 2024-12-20 cs.LG

Annie D'souza , Swetha M , Sunita Sarawagi This is my paper · ORCID

classification cs.LG

keywords classimbalancesynthetictabulardataoverlapregiondiffusionmodelsGANsclass-conditionalgenerationrandomforestdisagreementmachinelearningefficacy

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

The paper claims that one preprocessing step—splitting the majority class into a clear part and an overlap part that sits on the boundary with the minority class—markedly improves deep generative models for imbalanced tabular data. It converts binary labels to ternary labels before training the generator, then drops the overlap class when training the downstream classifier. On four real datasets with extreme imbalance (minority around 1.5–2%) and four inherently imbalanced UCI datasets, the method raises classification accuracy for five different classifiers across five different generators, with the largest gains for diffusion models. The paper's own ablations show the gains come from two separate effects: the ternary labels make the generator produce higher-quality minority examples, and excluding overlap majority points from classifier training declutters the decision boundary. A careful reader would care because the method is a cheap, generator-agnostic add-on rather than a new architecture.

What carries the argument

The load-bearing mechanism is the overlap class $D_{01}$, defined as the subset of majority instances whose label is uncertain under a k-fold cross-validated random forest. For each fold, a random forest is trained on the other majority folds plus all minority instances; a majority point whose predicted majority probability falls below $1-\tau$ is declared overlapping and relabeled as class 2. The size of $D_{01}$ is capped by $\min(\text{minority count}, r\% \text{ of majority count})$, with $r$ tuned on a validation set per generator. This ternary labeling is then fed to any class-conditional generator, and the paper's CTabSyn variant adds the target-class embedding to the TabSyn denoiser input. The overlap class does double work: it teaches the generator where the minority boundary is, and its removal from classifier training acts as a principled under-sampling of boundary points.

What would settle it

Compute the true overlap region on a synthetic Gaussian-blob dataset where the Bayes boundary is known, then compare the random forest's overlap set with the truth. If ORD's gains disappear when the true overlap set is used instead of the estimated one—or if the estimated set is far from the truth yet gains remain—then random-forest disagreement is not the mechanism the paper claims.

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Extended reading notes

Core claim

The central discovery is that deep generative models trained on imbalanced tabular data generate minority instances of far lower quality than majority instances, and that this failure can be substantially corrected without changing the generator's architecture. The correction is to label a subset of majority points that lie in the overlap region between the two classes as a third class, train the generator conditionally on these three classes, and then train the final classifier only on generated minority and clear-majority points plus real minority points. The paper demonstrates this through a paired comparison across CTGAN, CTABGAN+, TabDDPM, TabSyn, and ForestFlow, reporting accuracy gains in 26 of 32 (dataset, generator) combinations and a paired t-test p-value of 6.84e-05. It also provides visual and quantitative evidence on toy Gaussian-blob datasets where the true Bayes labels are known, showing that ORD reduces the fraction of wrongly generated minority points.

Load-bearing premise

A cross-validated random forest's disagreement score reliably marks where the two classes genuinely overlap, and the overlap size chosen on a validation set carries over to new data.

Editorial extensions

If this is right

ORD is a drop-in preprocessing layer: any existing tabular synthesizer gains accuracy when trained on the ternary-labeled data, so future generators can inherit the benefit without architectural change.
Classifiers trained on ORD-augmented data improve in macro-averaged accuracy, with the largest minority-accuracy gains on diffusion models such as TabSyn and ForestFlow.
Augmenting synthetic data with real minority points $D_1$ further improves the average of four classifiers, suggesting that real minority examples carry information the generator still misses.
The overlap region is dataset-specific: the optimal threshold $\tau$ varies (0.3 for Adult, lower for Cardio), so validation-based selection of $r$ is needed in practice.
ORD is not a simple boundary shift: tuning the decision threshold of a classifier trained without ORD does not match ORD's accuracy in the paper's comparison.

Reading between the lines

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

Editorial inference: the overlap-detection idea could extend to multiclass settings by pairwise or one-vs-rest overlap labeling; a natural test is whether per-class ternary splits preserve the same gains for non-binary targets.
Editorial inference: because the method works by making the generator's conditioning signal finer, other forms of auxiliary boundary information—such as density-ratio estimates or calibrated probabilities—might substitute for random forest disagreement and give larger gains.
Editorial inference: a strong testable extension is to replace the random forest with an explicitly calibrated classifier; if gains persist with a calibrated overlap detector, then miscalibration of the random forest is not the limiting factor.
Editorial inference: the method's reliance on validation-tuned $r$ per generator implies a small search cost; a practical extension would be a cheap heuristic for $r$ based on class-overlap statistics rather than a grid search.

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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: this is a solid, useful empirical paper with a simple idea—relabel boundary majority examples as a third "overlap" class before training deep tabular generators—and the broad evaluation largely backs it up. The missing control is a random split of the majority into two classes; without it the paper cannot support the claim that overlap semantics, rather than any finer-grained majority split, drive the gains. That is the main thing to ask for.

What is actually new: to my knowledge nobody has applied this ternary relabeling to deep tabular generators for imbalance. The paper shows consistent gains across CTGAN, CTAB-GAN+, TabDDPM, TabSyn, and ForestFlow on four constructed-imbalance datasets, with a paired t-test p=6.8e-5. The toy experiments with a known Bayes oracle are the strongest evidence: ORD improves minority generation accuracy for every generator in Table 3. The ablations—removing overlap on real data improves the classifier, threshold sensitivity in the appendix—are reasonable. Code is released, and the appendix includes standard deviations over runs.

Soft spots, in order: 1. No control for an arbitrary third class. If you split the majority randomly into two pseudo-classes during generator training, do you get the same gain? The paper never tests this. Appendix Table 12 addresses classifier boundary shifting, not generator conditioning. This is the key missing experiment for the paper's explanatory claim. 2. The "ORD before vs after synthesis" ablation (Table 6) is confounded, as the stress-test note says: applying the real-data overlap filter to binary-generated data removes an uncontrolled number of points, and Adult drops to 65.32 vs the 78.28 baseline. That table should be redone with a matched number of removed points or a random filter. 3. The r parameter is tuned per generator on validation; acknowledged, but it means the headline comparison is not fully parameter-free. Minor. 4. The novelty overclaim—"first work to harness deep generators for imbalanced data"—ignores the cited CTGAN-based imbalanced papers. Minor, but easy to fix. 5. Main tables lack error bars; the appendix std devs are there, but readers should not have to dig.

The central empirical finding—that adding an overlap class helps generator quality and classifier accuracy—holds up reasonably well. The mechanism is underdetermined, not wrong. This paper is for anyone working on tabular synthetic data or imbalanced classification; it is a practical preprocessing trick that is easy to adopt.

Recommendation: send to peer review. It is empirically rich, reproducible, and the main objection is addressable with one or two control experiments. A referee should push for the random-split control but not desk-reject this.

Referee Report

4 major / 5 minor

Summary. The paper proposes ORD, a preprocessing step for imbalanced tabular classification with generative data augmentation. ORD detects a subset of majority points that overlap the minority class using random-forest disagreement in k-fold cross-validation, relabels the training set as ternary (minority, overlap majority, clear majority), trains a class-conditional generator on those labels, and then trains the downstream classifier on balanced synthetic samples from minority and clear majority plus real minority points. The authors report improved classifier accuracy on four datasets across five generators, improved oracle-labeled quality of generated minority points on toy data, and ablations that attribute the gains to both improved generator quality and classifier-side downsampling of overlap points.

Significance. If the central claim is upheld, ORD is a simple, generator-agnostic intervention with practical value for imbalanced tabular data, and the observation that deep generative models degrade on minority classes is useful. Strengths include public code, multiple generators and classifiers, a paired statistical test, and oracle-based quality evaluation on toy data where the true Bayes label is known. However, because the active-ingredient claim is not tested against a placebo split of the majority class, the proposed explanation for the gains is currently unestablished, and a key ablation is confounded.

major comments (4)

[Section 4.1 and Table 2] The central claim is that labeling the true overlap region as a third class improves generator quality, but the paper never compares ORD against a control in which the majority class is split at random (or by a non-overlap rule) into two labels with the same number of points. Without this placebo control, the gains in Table 2 cannot be attributed to the overlap region specifically; they could come from any fine-grained split of the majority class making the conditional generation task easier, or from the classifier-side removal of a subset of majority points. Please add a randomized-split control that matches the size of D01 for each dataset and generator, and report the paired comparison against ORD.
[Table 6] The 'ORD before vs after synthesis' ablation is confounded. In the 'after' condition, applying the real-data overlap detector to binary-generated data removes an uncontrolled number of generated majority points; for Adult the resulting XGBoost accuracy (65.32) is far below the binary baseline (78.28 in Table 2), so the drop cannot be attributed solely to the absence of ternary training labels. The authors should control for the number of removed points, for example by also removing the same number of majority points at random from binary-generated data, and should report how many points were filtered in each condition.
[Section 4.1 and Appendix Table 13] The parameterization of the overlap-set size is inconsistent as written. Section 4.1 states that 'the number of overlapped majority points = min(number of minority points, r% of majority points)', with r values 3, 5, 7, 9, but Appendix Table 13 reports overlap counts for Adult and Cardio at thresholds 0.20-0.45 that do not satisfy this formula. For Adult, 3% of the majority count (34,514) is 1,035, yet the largest count in Table 13 is 915 at threshold 0.20; for Cardio, threshold 0.30 yields 700, which equals the minority count if that count is 700, but then the min formula would cap at that value. The relationship between tau and r is therefore not reproducible as written. Please define the exact procedure used to set tau/r and report the resulting overlap counts for the main experiments.
[Section 4.1 and Experimental Setup] The selection of the r parameter is not fully specified. The text says r is chosen by 'checking the performance on the validation set of a dataset' and then 'use the best r value with all datasets for a given synthesizer,' but it does not state which dataset's validation set is used, whether the same validation split is also used to tune baselines, or whether the reported test results are on data never used for r selection. If r was tuned on the same data used to report the main accuracy numbers, the paired t-test is not a fully held-out comparison. Please specify the exact protocol and, ideally, report the main results for each of the four candidate r values across all datasets and generators, since Appendix Tables 14-15 cover only Adult and Cardio with two synthesizers.

minor comments (5)

[Table 1] The column semantics in Table 1 are unclear: 'Min/Maj' appears to be the ratio of the undersampled minority to the majority, but the Cardio row gives an 'Unb Min' value of 100 while the text and Appendix Table 13 suggest a minority count around 700; please clarify and correct the table.
[Figure 1 and Algorithm 1] Figure 1 says 'use k-fold training to identify the overlap in the validation set,' but Algorithm 1 splits only the majority instances D0 into folds within the training set; the terminology should be made consistent.
[Main tables] The main accuracy tables do not show error bars, and the discussion of gains and losses does not reference the standard deviations in Appendix Table 11. For example, Table 2 shows CTGAN ORD on Heloc dropping from 62.93 to 61.47, and CTAB-GAN+ ORD on Fintech dropping from 57.45 to 54.83; these decreases should be acknowledged when claiming a gain in accuracy.
[Abstract and Section 5] The abstract and Section 5 refer to an 'Oracle Bayesian model,' but Table 4 uses an XGBoost classifier trained on real balanced data, not a Bayes classifier; the terminology should be adjusted to avoid overclaiming.
[Contribution list] The paper lists CTabSyn as 'a new SOTA model,' but Table 2 shows that on Fintech the average-of-4 accuracy of CTabSyn without ORD (65.37) is higher than that of CTabSyn ORD (63.58); the SOTA claim should be qualified to the settings where it is actually supported.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ORD's gains are empirical, evaluated on held-out test data, and the overlap labels, oracle quality checks, and validation-tuned r do not reduce to the evaluation outcome.

full rationale

The paper's claims are empirical rather than derived from an equation-level chain, and no load-bearing step is equivalent to its inputs by construction. The overlap set D01 is defined by random-forest disagreement (Section 4.1), but the final classifier is evaluated on real held-out test sets, and the generator quality is measured either on toy data with a known Bayes-optimal label (Table 3) or with a separately trained XGBoost oracle on balanced real data (Table 4), not with the same fitted labels used to train the generator. The r parameter is selected on a validation set from {3%,5%,7%,9%} and then fixed across datasets for each synthesizer; this is standard hyperparameter selection, not a fitted input renamed as a prediction. There are no self-citations carrying the central premise, no imported uniqueness theorems, and no ansatz smuggled in via prior work by the same authors. The skeptic's concern that no arbitrary-third-class control was run is a legitimate threat to the interpretation of the ablation, but it is an experimental-validity critique, not a circularity: the paper's results could be explained by a different mechanism without any step being definitionally forced. Accordingly, the appropriate circularity score is 0.

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

The central claim rests on a small number of tunable parameters (r, tau, k) and on two domain assumptions: that random forest confidence identifies overlap, and that ternary labels help generative models. No new physical or mathematical entities are introduced. The r parameter is explicitly fit to a validation set, which is a source of potential overfitting.

free parameters (3)

r = 3%, 5%, 7%, 9% (best selected per generator via validation)
Determines the target number of overlap points as min(minority count, r% of majority count). Chosen by validating on a held-out set for each generator (Section 4.1).
tau = 0.3 for real datasets, 0.2 for toy
Random forest confidence threshold for labeling overlap. In practice the threshold is adjusted to achieve the r-determined overlap count, so tau and r are coupled.
k = 2
Number of folds for random forest overlap detection (Section 5, Experimental Setup).

assumptions (3)

domain assumption Random forest disagreement on confidence identifies the true overlap region between majority and minority distributions.
This is the core heuristic of ORD. It is not proved; it is motivated by the idea that boundary points are hard to classify. The paper cites no theoretical guarantee (Section 4.1).
domain assumption Adding a finer-grained third class label improves the generative model's ability to model the minority distribution.
The paper demonstrates this empirically, but it is an assumption about the inductive bias of deep generative models, not a derived result.
domain assumption The validation-based selection of r generalizes to the test set.
r is chosen by checking performance on a validation set. This assumes no overfitting to the validation set.

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Cite this review

Pith. "Pith review of Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class." pith.science (2026). https://pith.science/paper/VRONQK4J

@misc{pith2026241215657,
  author       = {Pith},
  title        = {Pith review of: Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VRONQK4J}},
  note         = {Machine review of arXiv:2412.15657}
}

read the original abstract

Handling imbalance in class distribution when building a classifier over tabular data has been a problem of long-standing interest. One popular approach is augmenting the training dataset with synthetically generated data. While classical augmentation techniques were limited to linear interpolation of existing minority class examples, recently higher capacity deep generative models are providing greater promise. However, handling of imbalance in class distribution when building a deep generative model is also a challenging problem, that has not been studied as extensively as imbalanced classifier model training. We show that state-of-the-art deep generative models yield significantly lower-quality minority examples than majority examples. %In this paper, we start with the observation that imbalanced data training of generative models trained imbalanced dataset which under-represent the minority class. We propose a novel technique of converting the binary class labels to ternary class labels by introducing a class for the region where minority and majority distributions overlap. We show that just this pre-processing of the training set, significantly improves the quality of data generated spanning several state-of-the-art diffusion and GAN-based models. While training the classifier using synthetic data, we remove the overlap class from the training data and justify the reasons behind the enhanced accuracy. We perform extensive experiments on four real-life datasets, five different classifiers, and five generative models demonstrating that our method enhances not only the synthesizer performance of state-of-the-art models but also the classifier performance.

Figures

Figures reproduced from arXiv: 2412.15657 by the authors.

**Figure 2.** CTabSyn for class conditional tabular data gener [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Visualisation of Synthetic data for ORD in 2D datasets. The first row shows data synthesized with ORD for different Synthesizers along with a clear indication of the overlap class D01. The second row zooms only on sampled minority with wrong generations marked in pink from generators without ORD. The third row shows the same data but with ORD enhanced generators. The columns correspond to different generators. Parti… view at source ↗

Figures from the paper (9 more)

**Figure 5.** Figure 5: MLE of dataset cardio with varying thresholds dur [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 6.** Figure 6: Varying thresholds changes the number of [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

**Figure 7.** Figure 7: Toy 2 visusalisation showing ORD on different [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

**Figure 8.** Figure 8: Toy 2 visusalisation [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]

**Figure 9.** Figure 9: The distribution of the 1D mixture of gaussians [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]

**Figure 10.** Figure 10: The distribution of the 1D mixture of Gaussians with the overlap majority class detected were taken from Kaggle3 . The next four are UCI imbalanced datasets - Abalone, Yeast, Car Evaluation and Bank Marketing. The statistics of these datasets have been presented in …

**Figure 11.** Figure 11: The outputs of using 2 classes and 3 classes and with and without a classifier loss contains objective information i.e. factual information, subjective information i.e. information given by patients and results of medical examinations. Fintech6 : This dataset contai…

**Figure 12.** Figure 12: TABDDPM Schematic Diagram Result Analysis: TabDDPM consistently outperforms TVAE and CTABGAN+ on diverse datasets, highlighting the advantage of diffusion models for tabular data across various domains. The SMOTE method exhibits competitive performance comparable to …

**Figure 13.** Figure 13: TABSYN Schematic Diagram Result analysis The experiments compared TABSYN with seven existing tabular data generation methods, including classical GAN and VAE models, and recent state-of-theart methods. TABSYN consistently outperformed all baseline methods in a stan…

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Reference graph

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

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, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

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