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REVIEW 4 major objections 4 minor 9 cited by

Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper argues that tabular data-centric AI reduces to two data transformations — feature selection and feature generation — and that one taxonomy can organize traditional, reinforcement-learning, and generative methods for both.

desk verdict A useful and current survey frame for tabular feature selection and generation, but the broken citation-reference machinery undercuts its reliability as a reference work and needs a serious pass before it can serve its purpose. read the letter →

arxiv 2501.10555 v1 pith:2JIFUQNR submitted 2025-01-17 cs.LG cs.AI

Dongjie Wang , Yanyong Huang , Wangyang Ying , Haoyue Bai , Nanxu Gong , Xinyuan Wang , Sixun Dong , Tao Zhe
show 6 more authors
Kunpeng Liu Meng Xiao Pengfei Wang Pengyang Wang Hui Xiong Yanjie Fu
This is my paper · ORCID
classification cs.LGcs.AI
keywords data-centricAItabulardatafeatureselectiongenerationreinforcementlearninggenerativeautomatedengineeringsurvey
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

This survey claims that the practical route to better AI on tabular data runs through the data itself, not just the model. It argues that tabular data transformation has two core tasks — feature selection, which keeps useful columns and drops redundant ones, and feature generation, which creates new columns from existing ones — and that all current methods fit into a single taxonomy. The survey places classical filter, wrapper, and embedded selection alongside multi-view variants, then shows how reinforcement learning and generative AI recast both tasks as automated search problems. It closes with a comparison table and practical guidelines. If the synthesis is right, a practitioner can look up any tabular-data pain point and find the family of solutions designed for it.

What carries the argument

The load-bearing object is the survey's taxonomy, summarized in Figure 2, which partitions tabular data-centric AI into feature selection and feature generation, then under each into traditional methods (filter, wrapper, embedded, hybrid, and multi-view) and advanced methods (reinforcement learning and generative AI). Two formal mechanisms carry the advanced half: the Markov decision process formulation, whose state is the current feature set (plus transformation history for generation), whose actions are selecting or deselecting a feature or applying a mathematical operation, and whose reward balances downstream performance against set size or complexity; and the encoder-decoder-evaluator formulation, in which transformation sequences are encoded into a continuous embedding, decoded back to sequences, and evaluated by predicted performance, enabling a gradient-based search for better transformations.

What would settle it

A reader can test the survey's utility claim directly by resolving every citation marker in Section 3.1.1 and Figure 2 against the reference list; broken markers — such as the maximum-relevance minimum-redundancy method cited only as '[? ]' and figure entries whose numbers do not correspond to the listed references — would falsify the claim that this is a dependable map of the field.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that feature selection and feature generation are the two essential operations of tabular data-centric AI, and that prior surveys covered only part of this picture. It claims to bridge those gaps by integrating traditional methods, reinforcement learning, and generative AI for both operations under one taxonomy. Feature selection is formalized as choosing a subset $S \subseteq \{1,\dots,d\}$ of the $d$ original features, and feature generation as producing a transformed dataset through mathematical operations. Advanced methods are then formulated as Markov decision processes for reinforcement learning — the state is the current feature set, the action selects or deselects a feature or applies a transformation, and the reward balances downstream performance against penalties — and as an encoder-decoder-evaluator architecture for generative AI, where transformation sequences are mapped to a continuous embedding space, reconstructed, and scored so that better transformations can be found by gradient-based search. The paper also identifies open challenges, including scalability, interpretability, privacy-preserving feature engineering, and LLM-based generation.

Load-bearing premise

The survey's usefulness rests on its descriptions and citations of prior work being accurate and correctly attributed, because a reader who cannot trust the mapping from method to citation cannot act on the taxonomy.

Editorial extensions

If this is right

  • A practitioner facing a high-dimensional table can use the taxonomy to place their problem: cheap statistical screening first, wrapper or embedded selection when feature interactions matter, reinforcement learning for sequential or dynamic feature decisions, and generative models for transferring feature knowledge across tasks.
  • Feature selection and feature generation, usually treated as separate pipelines, can be viewed as one data-space refinement problem with a shared optimization language.
  • Reinforcement-learning-generated feature transformation records can be compressed into an embedding space, meaning experience from many datasets can be reused instead of rediscovered.
  • LLM-based feature generation can operate without a separate learned predictor, using in-context learning and retrieval-augmented generation from external knowledge to create explainable features.
  • The comparison of traditional and advanced methods gives concrete guidance: prefer traditional methods for small, static, interpretability-critical datasets and advanced methods for high-dimensional, dynamic, or multimodal ones.

Reading between the lines

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

  • If the unified formulations are taken seriously, a natural next step the paper does not take is a shared benchmark that scores feature-selection and feature-generation methods on identical tabular datasets, which would make the taxonomy testable rather than descriptive.
  • The encoder-decoder-evaluator pattern suggests a testable extension: pretraining the embedding space on synthetic transformation records from many datasets should improve downstream feature quality on a held-out tabular task, and failure would indicate the embedding is not capturing transferable knowledge.
  • The survey's emphasis on interpretability points to an under-explored hybrid: generative methods that produce candidate features but require a domain expert's validation before entering the model, potentially combining scalability with trust.
  • The LLM-based directions imply that text-informed feature generation will matter most in domains where tabular rows come with side text, such as medical records or product descriptions; a controlled comparison against text-free generation on such data would quantify the gain.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper surveys tabular data-centric AI, focusing on feature selection and feature generation. It organizes feature selection into single-view (filter, wrapper, embedded, hybrid) and multi-view (supervised, semi-supervised, unsupervised) methods, and feature generation into human-driven and automated techniques. It then reviews advanced methods based on reinforcement learning and generative AI, provides an MDP formulation for RL-based feature engineering, an encoder-decoder-evaluator formulation for generative approaches, a comparative analysis with practical guidelines, and a discussion of open challenges such as AutoML, LLMs, and federated learning.

Significance. The topic is timely and the paper's architecture is a real strength: it unifies feature selection and generation under a single taxonomy, distinguishes traditional, RL, and generative approaches, and gives explicit formalizations in Section 5. The comparative discussion in Section 6 and the practical guidelines in Section 6.2 are useful and broadly accurate at a high level. If the citation apparatus were reliable, this would be a valuable reference for researchers entering tabular data-centric AI. The manuscript makes no novel algorithmic claims, so there are no derivations or fitted parameters to verify; its evidentiary burden is entirely attributional, which is exactly where the current version fails. The breadth of coverage is genuine, but the broken citations mean the paper cannot currently serve as the trustworthy comprehensive reference it sets out to be.

major comments (4)
  1. [§3.1.1 and Figure 2] The citation apparatus for the filter-based methods is internally inconsistent and contains a placeholder. The text cites the chi-square test as [38, 99], ANOVA as [77, 132, 155], Pearson correlation as [193], and mutual information as [5, 193], while Figure 2 attributes the same methods to [45, 46], [47, 48, 49], [50], and [50, 51], respectively. In addition, the sentence "Methods like mRMR (Maximum Relevance Minimum Redundancy) [? ]" contains a literal unresolved placeholder. Because a survey's primary function is to let readers locate and verify the methods it describes, these inconsistencies make the central claim of a reliable comprehensive survey unverifiable as submitted. The authors should audit every in-text citation against the final reference list.
  2. [References [32]/[33], [54]/[55], [91]/[92]] The reference list contains at least three duplicate entries: [32] and [33] are the same Fan et al. paper, [54] and [55] are the same Huang et al. paper, and [91] and [92] are the same Liu et al. paper. These duplicates are not harmless: Section 5.1.1 cites [92] for the Monte Carlo early-stopping method while [91] is the identical paper, and Section 3.1.4 cites [55] for the genetic-algorithm-plus-mutual-information hybrid while [54] is the same work. The list must be deduplicated and all downstream numbering recomputed.
  3. [§5.1.1 and Figure 2] The advanced RL feature-selection entries disagree between text and taxonomy figure. The text attributes the multi-agent framework to Liu et al. [89], the GCN-based collaboration to [90], the group-wise method to Fan et al. [34], IRFS to [35], and decision-tree feedback to [33], whereas Figure 2 lists `Group-Wise Method [8]`, `Advanced Statistical Summaries & GCNs State Representation [39]`, `Enhanced Reward Scheme [173]`, `Interactive Reinforced Feature Selection [7]`, `Pre-Filtering And Iterative RL [177]`, and `Decision Tree Based [178]`. Some of these entries point to papers unrelated to the cited method; for example, [8] is a pattern-recognition textbook and [39] is a forest-optimization paper. The same problem affects the LLM branch, where the text cites [189] and [188] but Figure 2 cites [190] and [191]. This makes it impossible to verify the survey's coverage of advanced methods.
  4. [Figure 2 vs Section 4] Figure 2 promises a `Latent Representation Learning` category under traditional feature transformation, with subcategories for autoencoders, categorical embeddings, and PCA, but Section 4, the corresponding textual treatment, contains no such subsection. PCA appears only later as a dimensionality-reduction suggestion in Section 6.2, and autoencoders appear only in the feature-selection discussion, e.g., SDAE-LSTM in Section 3.1.3. Either a substantive subsection is missing or the taxonomy overstates the coverage; the paper should align the figure with the text before claiming comprehensiveness.
minor comments (4)
  1. [Section 1] The phrase `F or feature selection` contains a stray capitalization artifact and should read `For feature selection`.
  2. [Figure 2 and Section 4] The figure labels a whole branch `Feature Transformation`, while the text consistently uses `Feature Generation`; please unify the terminology.
  3. [Section 5.1] The reward formula for feature selection is split across a line break in a way that could confuse readers; the expression `R(t) = Perf(...) - lambda * |F(t+1)|` should be set on one line with proper punctuation.
  4. [Reference list] Several entries, including [41], [56], [154], [174], [188], and [189], are arXiv preprints; please mark them clearly as preprints and include access dates.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a descriptive survey with no fitted parameters, no derivation chain, and no prediction that reduces to its own inputs; citation-reference mismatches are a verifiability issue, not circularity.

full rationale

This manuscript is a survey, not a derivation or an empirical study. It organizes existing work on tabular data-centric AI into a taxonomy, describes methods from the literature, and provides comparative guidance. There is no fitted parameter that is later renamed as a prediction, no result claimed to follow from first principles, and no equation in the paper is shown to be equivalent to an input by construction. The formal definitions in Section 2 and the RL/generative AI formulations in Section 5 are general mathematical descriptions of problem settings, not derivations of new results. The self-citations to the authors' prior works (e.g., [147], [148], [165], [175], [177], [178]) are used to describe those works as part of the surveyed landscape, not as the sole justification for a new central claim, so they are not load-bearing in a circularity sense. The significant citation-reference mismatches, duplicate references, and the literal '[? ]' placeholder for mRMR that the reviewer identified are real correctness and verifiability defects, but they are not instances of the seven circularity patterns: they do not make any claimed result equivalent to its own input. Under the hard rules, this non-finding is the appropriate outcome, and the circularity score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

A survey introduces no free parameters or invented entities. It relies on the accuracy of its citations and the adequacy of its taxonomy as domain assumptions.

assumptions (2)
  • domain assumption The survey's characterizations of cited methods are faithful to the original publications.
    The entire utility of the survey rests on accurate representation of prior work; any mischaracterization would mislead readers.
  • domain assumption The taxonomy categories (filter/wrapper/embedded, single/multi-view, RL/generative) are exhaustive and non-overlapping.
    The claim of comprehensiveness depends on the taxonomy covering all relevant methods without omission.

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Cite this review

Pith. "Pith review of Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation." pith.science (2026). https://pith.science/paper/2JIFUQNR

@misc{pith2026250110555,
  author       = {Pith},
  title        = {Pith review of: Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2JIFUQNR}},
  note         = {Machine review of arXiv:2501.10555}
}
read the original abstract

Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric AI, improving data quality and representation has become essential for enhancing model performance, particularly in applications centered around tabular data. This survey examines the key aspects of tabular data-centric AI, emphasizing feature selection and feature generation as essential techniques for data space refinement. We provide a systematic review of feature selection methods, which identify and retain the most relevant data attributes, and feature generation approaches, which create new features to simplify the capture of complex data patterns. This survey offers a comprehensive overview of current methodologies through an analysis of recent advancements, practical applications, and the strengths and limitations of these techniques. Finally, we outline open challenges and suggest future perspectives to inspire continued innovation in this field.

Figures

Figures reproduced from arXiv: 2501.10555 by the authors.

Figure 1
Figure 1. Real-world applications often generate vast amounts of tabular data, making Data-Centric AI essential for optimizing [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. An overview of the taxonomy for existing tabular data-centric AI techniques. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Feature selection is framed as a RL problem, wherein the agent’s actions correspond to the selection of individual features. [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Feature generation is formulated as a RL problem, where the agent’s actions involve selecting individual features and applying [PITH_FULL_IMAGE:figures/full_fig_p020_4.png]
Figure 5
Figure 5. Figure 5: RL extensively explores the feature space, generating abundant feature learning (feature selection/generation) knowledge. [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]

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Forward citations

Cited by 9 Pith papers

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.