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arXiv · 2605.03430

DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data

Al Zadid Sultan Bin Habib·Gianfranco Doretto·Donald A. Adjeroh
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Abstract

High-dimensional tabular data lacks a natural feature order, limiting the applicability of permutation-sensitive deep learning models. We propose DynaTab, a dynamic feature ordering-enabled architecture inspired by neural rewiring. We introduce a lightweight criterion that predicts when feature permutation will benefit a dataset by quantifying its intrinsic complexity. DynaTab dynamically reorders features via a neural rewiring algorithm and processes them through a compact, dynamic order-aware combination of separate learned positional embedding, importance-based gating, and masked attention layers, compatible with any sequence-sensitive backbone. Trained end-to-end with bespoke dynamic feature ordering (DFO) and dispersion losses, DynaTab achieves statistically significant gains, particularly on high-dimensional datasets, where it is benchmarked against 45 state-of-the-art baselines across 36 different real-world tabular datasets. Our results position DynaTab as a compelling new paradigm for high-dimensional tabular deep learning.

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Al Zadid Sultan Bin Habib, Gianfranco Doretto, Donald A. Adjeroh. 2026-05-05. DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data. https://arxiv.org/abs/2605.03430

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