Evaluating the Effectiveness of SechKAN on 1D Data
cs.LG · cs.AI
Quick verdict
SechKAN matches CNN/MLP/EfficientKAN at ~matched params on three 1D sets, best on Crop; fair small study, but no significance tests and asymmetric tuning.
Summary
The paper empirically evaluates SechKAN, a Kolmogorov-Arnold Network variant that uses hyperbolic secant basis functions plus a 1D grid projection to keep parameter counts near MLP levels, on three 1D classification benchmarks: UCI HAR (561-dimensional features, 6 classes), ElectricDevices (length-96 univariate series, 7 classes), and Crop (length-46 series, 24 classes). Compared against EfficientKAN, MLP, CNN1D, ResNet1D, and DSCNN1D at roughly matched parameter budgets (about 147k, 27k, and 19k parameters respectively), SechKAN reaches test accuracies of 94.84 ± 0.86 on UCI HAR, 62.75 ± 0.85 on ElectricDevices, and 73.48 ± 0.41 on Crop, the last being the highest among all evaluated models. Ablations over grid size (2–32) and normalization show dataset-dependent optima (grid 16 with BatchNorm for Crop, grid 4 elsewhere) and that BatchNorm at Norm1 beats no normalization on all three datasets. Training uses AdamW and OneCycleLR for 20–30 epochs on an RTX 3060 Ti; results are means ± standard deviations over five seeds, and code is public. Limitations: only three datasets, classification only, a single KAN baseline, no statistical significance testing, an undocumented selection procedure for per-dataset SechKAN configurations, and an unexplained validation-test gap on ElectricDevices.
Review complete
Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- SechKAN achieves competitive test accuracy against EfficientKAN, MLP, CNN1D, ResNet1D, and DSCNN1D at approximately matched parameter budgets on UCI HAR, ElectricDevices, and Crop (§4.2, Table 2).
- SechKAN obtains the highest mean test accuracy among the evaluated models on Crop (73.48 ± 0.41), while ResNet1D leads on ElectricDevices (69.86 ± 0.61).
- SechKAN maintains a favorable accuracy-training-time trade-off, combining top Crop accuracy with relatively low training time (§4.2, Fig. 2).
- Grid size and normalization placement materially affect SechKAN performance, with best configurations varying across datasets and BatchNorm at Norm1 best on all three datasets (§4.3, Figs. 3–4).
Strengths
Transparent, seed-controlled experimental protocol
Canonical splits kept untouched (subject-disjoint 20% validation holdout on UCI HAR with seed 42, §4.1), five training seeds (0–4) with mean ± std in Table 2, and fully specified optimizer settings (AdamW lr 1e-3, wd 1e-4, batch 64, OneCycleLR).
Genuinely matched parameter budgets
Table 1 shows tight ranges per dataset — 147,038–147,420 params on UCI HAR, 26,780–27,359 on ElectricDevices, 18,200–18,874 on Crop — with per-model layer structures listed, making the comparison fair on model size.
Public code and public data enable verification
Candid limitations section
Ablations isolate two design axes
Weaknesses
No statistical significance testing behind the headline claim
Undocumented, asymmetric configuration selection
Unexplained validation-test gap on ElectricDevices
Undefined timing metric and normalized-only ablation reporting
Single KAN baseline despite citing stronger alternatives
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Scope & Claims
This is a focused empirical follow-up to the author's SechKAN paper (Neurocomputing 2026, ref [24]). The claim set is deliberately modest: (1) SechKAN is "competitive" with EfficientKAN, MLP, CNN1D, ResNet1D, and DSCNN1D at approximately matched parameter budgets on three 1D classification datasets (UCI HAR, ElectricDevices, Crop); (2) it is best on Crop; (3) grid size and normalization materially affect results (§4.3). Claims (1) and (3) are supported by the reported numbers; claim (2) is the weakest statistically (below). The Limitations section (§5) is candid about scope: three datasets, classification only, one KAN baseline, no exhaustive hyperparameter search.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is SechKAN?
SechKAN is a Kolmogorov-Arnold Network (KAN) variant that uses hyperbolic secant (sech) functions as learnable basis functions, combined with a 1D grid projection that compresses the grid dimension so the layer's parameter count stays close to that of an MLP.
Does SechKAN beat CNNs and MLPs on 1D data?
On the three datasets tested (UCI HAR, ElectricDevices, Crop), it was competitive at matched parameter counts: highest test accuracy on Crop (73.48 ± 0.41), near the best on UCI HAR (94.84 ± 0.86), and mid-pack on ElectricDevices (62.75 ± 0.85), where ResNet1D led with 69.86 ± 0.61. The Crop margin over the runner-up is small and untested for significance.
Is the SechKAN 1D paper's code and data available?
Yes. The paper links a public GitHub repository (github.com/hoangthangta/SechKAN_1D), and all three datasets are public benchmarks: UCI HAR and two datasets from timeseriesclassification.com (ElectricDevices, Crop), with canonical test splits.
What are the main limitations of this study?
Only three classification datasets, no regression or forecasting tasks, a single KAN baseline (EfficientKAN), no statistical significance tests, per-dataset hyperparameter configurations with an undocumented selection procedure, and no analysis of the large validation-test accuracy gap on ElectricDevices.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures 3–4 are extractable only as min-max normalized heat-map values; absolute ablation accuracies and per-seed spreads cannot be verified from the text.
- The unit/definition of the 'Time (s)' column in Table 2 is not stated in the text, so all training-efficiency remarks are relative only.
- Review is based on arXiv v1 full text; supplementary materials or per-run logs referenced by the repo were not examined.
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