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REVIEW 3 major objections 6 minor 67 references

ODTE -- An ensemble of multi-class SVM-based oblique decision trees

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read ODTE, a bagging ensemble of SVM-based oblique decision trees, claims top accuracy and more compact trees than competing oblique ensembles and state-of-the-art classifiers on 49 datasets.

desk verdict Default results are honestly modest; the tuned-significance claim rests on an asymmetric and possibly leaky tuning protocol. read the letter →

arxiv 2411.13376 v2 pith:C2RF4ZD5 submitted 2024-11-20 cs.LG

Ricardo Montañana , José A. Gámez , José M. Puerta This is my paper · ORCID
classification cs.LG
keywords obliquedecisiontreessupportvectormachinesensemblelearningbaggingmulticlassclassificationone-vs-oneone-vs-resthyperparametertuning
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 a bagging ensemble of oblique decision trees with SVM-learned splits can outrank both specialized oblique-tree ensembles and general-purpose classifiers on tabular data. The base algorithm, STree, handles multi-class problems without clustering labels into artificial binary groups: at each node it trains several one-vs-one or one-vs-rest SVMs and keeps the single SVM whose induced split most reduces class impurity. On 49 datasets, the default ensemble ODTE posts the highest average accuracy (0.8391) and best average rank (3.88) among ten compared methods; the tuned version ODTET (rank 1.35) is statistically significantly better than all three tuned competitors after correction for multiple comparisons. STree's trees are also substantially more compact than the competitors' trees. If the tuned comparison is fair, the method is a strong, easily tunable option for structured-data classification.

What carries the argument

The load-bearing object is STree's node-level model selection. At a node with $k'$ classes, the algorithm enumerates either $r = k'(k'-1)/2$ one-vs-one SVM problems or $k'$ one-vs-rest problems, evaluates each learned SVM by the weighted impurity of the binary partition it induces, $$b^* = \arg\min_j \frac{|D'_+|}{|D'|} I(Y,D'_+) + \frac{|D'_-|}{|D'|} I(Y,D'_-),$$ and stores only the selected model. This selection turns the multi-class problem into a sequence of binary SVM splits without external clustering; bagging over many such trees produces the ensemble.

What would settle it

Re-run the ten-times five-fold cross-validation with the same ten seeds, giving each tuned baseline a grid search over the same hyperparameter ranges and number of configurations as ODTET. If ODTET no longer holds the best average rank or the adjusted post-hoc p-values climb above $\alpha = 0.05$, the paper's central superiority claim is falsified.

Watch

Extended reading notes

Core claim

The central discovery is a mechanism for building oblique trees that natively handle multi-class targets. Instead of reducing each node's data to a binary problem by grouping classes, STree fits one SVM for every one-vs-one pair (or every class versus the rest) and chooses the model whose partition minimizes weighted Shannon entropy. The selected hyperplane becomes the node test; recursion continues on the two sides. Wrapped in bagging with 100 trees, this yields ODTE. In the paper's 49-dataset, ten-times five-fold cross-validation study, ODTE has the best default mean accuracy and rank, and after per-dataset grid-search tuning, ODTET's superiority over the three tuned twin-bounded-SVM oblique competitors is statistically significant; normalized average tree size is 1.00 for ODTE versus 2.66 to 11.73 for its oblique competitors.

Load-bearing premise

The tuned comparison assumes that ODTET's grid search and the baselines' author-chosen hyperparameters represent equally thorough tuning; if ODTET received a more favorable search, the statistically significant tuned ranking would reflect tuning asymmetry rather than algorithm quality.

Editorial extensions

If this is right

  • In the tuned scenario, ODTET's rank of 1.35 beats all three tuned oblique competitors, and the adjusted post-hoc comparisons reject equivalence at $\alpha = 0.05$.
  • In the default scenario, ODTE has the best average accuracy and rank but is statistically significantly better only than XGBoost and TBRoF; against most other defaults the edge is not significant.
  • STree handles multi-class targets directly, so practitioners do not need to cluster classes into artificial binary groups before building oblique trees.
  • ODTE trees are the most compact among the compared oblique-tree methods, with normalized average size 1.00 versus 2.66 to 11.73 for competitors.
  • Both ODTE and STree are implemented as standard machine-learning library classifiers, so the method can be adopted and tuned with familiar tooling.

Reading between the lines

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

  • Because the default ODTE is not statistically distinguishable from most default competitors, the strongest claim rests on the tuned comparison; an equal-tuning comparison against RandomForest and XGBoost would reveal whether the margin is intrinsic to the algorithm or driven by tuning procedure.
  • STree's training cost scales with the number of classes: the one-vs-one strategy trains $O(k'^2)$ SVMs per node, so for datasets with many labels the one-vs-rest variant may be a substantially cheaper alternative that the paper does not systematically explore.
  • The reported compactness of ODTE trees likely translates into faster inference and a smaller memory footprint, but the paper reports tree size rather than measured inference latency; a latency benchmark is a natural extension.
  • The node-level selection of a single best SVM can be viewed as a greedy search over a restricted hypothesis space; combining it with random feature subspaces, as in Random Forest, is a testable direction the paper lists for future work.
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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

3 major / 6 minor

Summary. The paper proposes ODTE, a bagging ensemble of oblique decision trees, and STree, a single oblique tree whose splits are SVM classifiers selected via one-vs-one or one-vs-rest multiclass embeddings. The method is evaluated on 49 datasets against several oblique-tree ensembles and standard classifiers under 10x5 cross-validation. In the default configuration, ODTE obtains the best average accuracy (0.8391) and best Friedman rank (3.88), though Holm post-hoc tests show significance only against XGBoost and TBRoF. In the tuned configuration, ODTET has rank 1.35 and differs significantly from all tuned baselines. The paper also reports that ODTE trees are substantially more compact than competitors, and it releases code and datasets for reproducibility.

Significance. If the empirical claims hold, the contribution is a useful, practically integrated oblique-tree ensemble with a simple multiclass handling scheme and a compact-tree property. The paper deserves credit for a standard evaluation protocol (10x5cv with shared seeds, Friedman plus Holm post hoc), a large benchmark, and public code and data. The default-setting results are reported honestly, including non-significant comparisons. The load-bearing strength of the paper, however, is the tuned comparison, and that part rests on an incompletely specified and asymmetric tuning protocol, which currently prevents the reader from interpreting the Holm-significant results as evidence of algorithmic superiority. The compactness result is interesting but is presented without statistical testing.

major comments (3)
  1. [Section 4.2 and Table 4] The manuscript does not state whether the gridsearch for ODTET hyperparameters was nested inside each 10x5CV fold or performed on the full dataset before cross-validation. Since Table 4 reports 10x5CV accuracy on the same 49 datasets, any full-data hyperparameter selection would leak test-fold information and bias ODTET's accuracy, rank, and Holm p-values. The authors should specify the exact selection protocol; if selection used full datasets, the tuned comparison must be rerun with nested or fold-specific tuning.
  2. [Section 4.2 and Table 6] The tuning effort is asymmetric across algorithms: ODTET receives a fresh gridsearch over STree's hyperparameters per dataset, while TBRaFT, TBRoFT, and TBRRoFT reuse hyperparameter values selected by the original authors in their provided code. No search budget, search space, or model-selection criterion is reported, so the Holm p-values in Table 6 do not establish that ODTET dominates these algorithms as algorithms. A matched tuning protocol, or at least a sensitivity analysis with a common hyperparameter budget, is needed to support the 'significant performance gains when hyperparameters are carefully tuned' claim.
  3. [Section 4.4 and Table 5] In the default setting, ODTE's rank advantage over TBRRoF is 3.88 versus 3.95 with Holm p = 1.0, and ODTE is not significantly better than most baselines; only XGBoost and TBRoF are clearly outperformed. The abstract's phrasing 'ranks consistently above its competitors' is defensible as a rank statement, but the conclusion in Section 5 that ODTE 'emerges as the outstanding algorithm in the comparison' overstates the default-setting evidence. The language should be moderated to reflect that the default advantage is largely not statistically significant.
minor comments (6)
  1. [Section 4.1 / Table 1] Table 1 lists 'oocytes merluccius nucleus 4d' and similar fishery datasets; a brief reference or description of these datasets beyond the pointer to Ganaie et al. (2020) would improve self-containedness.
  2. [Algorithm 2, line 9] The pseudocode says 'I(·) is an information theory meassure'; the spelling should be 'measure'.
  3. [Section 4.2] The URLs for tuned hyperparameters and supplementary materials are short links; expanding them or placing them in a stable repository reference would improve reproducibility.
  4. [Section 4.4] Table 7 reports normalized tree sizes and training times without variance or statistical testing; a confidence interval or at least a per-dataset breakdown would help assess the stability of the compactness claim.
  5. [Section 5] The sentence 'there is no doubt that ODTE emerges as the outstanding algorithm' is too strong given the default-setting significance results; please soften it or qualify it as applying primarily to the tuned scenario after the tuning-protocol issue is resolved.
  6. [Throughout] There are several typographical errors, e.g., 'facilitte' in the introduction, 'bewteen' in Section 4.4, and 'SckitLearn' in Section 1; a careful proofreading pass is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: ODTE is an algorithmic contribution evaluated against external benchmarks, and its only self-citation is non-load-bearing.

full rationale

The paper does not derive ODTE's performance from an internal model whose parameters are fitted to the benchmark results; it presents a concrete algorithm (Algorithms 1 and 2), releases code and datasets, and evaluates 10x5CV accuracy on 49 external datasets against independent baselines. The only self-citation, 'A preliminary version of STree was presented in Montañana et al. (2021)' (Section 3), is contextual: the current manuscript fully specifies STree, so no load-bearing claim depends on the prior paper. The tuned comparison in Section 4.2 raises a legitimate experimental-fairness concern -- ODTET's gridsearch protocol and whether it was nested inside the cross-validation loop are not described, while TBRaFT, TBRoFT, and TBRRoFT reuse hyperparameters selected by the original authors -- but this is a comparison-quality issue, not circularity: the reported numbers are test-set accuracies, not quantities forced by construction. No fitted-parameter-renamed-as-prediction, no uniqueness theorem imported from the authors, and no ansatz smuggled via citation appear. The central claim is therefore self-contained with respect to the empirical evaluation reported.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The method introduces no new theoretical entities, forces, or formal objects. It relies on standard SVM, bagging, and information-theoretic machinery. The only experimenter-chosen quantities are hyperparameters and the choice of benchmark, which are standard in empirical ML papers.

free parameters (2)
  • STree hyperparameters (C, kernel, multiclass strategy, splitter, max features, max iter) = C=1, kernel=linear, strategy=OvO, splitter=random, max features=None, max iter=1e5
    These are model hyperparameters, not fitted constants, and they are default settings evaluated on held-out test folds. They are listed here because the tuned version per dataset is a free choice of the experimenters, though standard practice in ML evaluation.
  • Per-dataset tuned hyperparameters for ODTET = Available at https://t.ly/rEZLH
    The tuned accuracy results depend on per-dataset hyperparameter selection via gridsearch. These choices are part of the experimental comparison and are not derived from first principles.
assumptions (3)
  • domain assumption Accuracy is an appropriate performance measure for the benchmark datasets.
    Section 4.4 justifies this by citing that no severe class imbalance is present, referring to Table A1 of Ganaie et al. (2020). The validity of the comparison depends on this claim.
  • standard math The Friedman test with Holm post hoc procedure is an appropriate statistical framework for comparing classifiers across datasets.
    Invoked in Section 4.4 following Demsar (2006) and Garcia and Herrera (2008). This is standard practice in machine learning.
  • domain assumption The 49 datasets from Ganaie et al. (2020) are a representative benchmark for tabular classification.
    Section 4.1 states the datasets were previously used in Ganaie et al. (2020). The generality of the conclusions depends on this benchmark being representative.

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Cite this review

Pith. "Pith review of ODTE -- An ensemble of multi-class SVM-based oblique decision trees." pith.science (2026). https://pith.science/paper/C2RF4ZD5

@misc{pith2026241113376,
  author       = {Pith},
  title        = {Pith review of: ODTE -- An ensemble of multi-class SVM-based oblique decision trees},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C2RF4ZD5}},
  note         = {Machine review of arXiv:2411.13376}
}
read the original abstract

We propose ODTE, a new ensemble that uses oblique decision trees as base classifiers. Additionally, we introduce STree, the base algorithm for growing oblique decision trees, which leverages support vector machines to define hyperplanes within the decision nodes. We embed a multiclass strategy -- one-vs-one or one-vs-rest -- at the decision nodes, allowing the model to directly handle non-binary classification tasks without the need to cluster instances into two groups, as is common in other approaches from the literature. In each decision node, only the best-performing model SVM -- the one that minimizes an impurity measure for the n-ary classification -- is retained, even if the learned SVM addresses a binary classification subtask. An extensive experimental study involving 49 datasets and various state-of-the-art algorithms for oblique decision tree ensembles has been conducted. Our results show that ODTE ranks consistently above its competitors, achieving significant performance gains when hyperparameters are carefully tuned. Moreover, the oblique decision trees learned through STree are more compact than those produced by other algorithms evaluated in our experiments.

Figures

Figures reproduced from arXiv: 2411.13376 by the authors.

Figure 1
Figure 1. Different decision boundaries and their corresponding decision trees: (left) axis [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

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Reviewed August 12, 2026 · model on record in the stance chip above.