| Tabular classification is a central supervised learning problem in high-stakes domains. While tree ensembles are strong baselines and deep tabular models sometimes help, standard ensembles typically use global fusion (e.g., uniform averaging), implicitly assuming constant expert reliability across the input space. We propose a lightweight, architecture agnostic dynamic gating mechanism that performs instance-wise mixture weighting using raw covariates, expert probability vectors, and two confidence features (entropy and margin). Across 14 benchmark datasets, dynamic gating improves mean-ensemble accuracy from 0.9047 to 0.9149 in a heterogeneous pool (+0.0102) and from 0.9082 to 0.9126 in a tree-only pool (+0.0044), with 11/14 dataset wins in both settings. Aggregate paired Wilcoxon tests confirm significance (p = 2.44 ×10^−4 for heterogeneous, p = 1.07 ×10−2 for tree-only), showing that compact instance-wise weighting improves robustness without architecture-specific fusion. |
*** Title, author list and abstract as submitted during Camera-Ready version delivery. Small changes that may have occurred during processing by Springer may not appear in this window.