Dynamic enhanced binning framework

Abstract

This paper proposes Dynamic Enhanced Binning (DEB), a structured white-box framework designed to bridge the gap between the accuracy of Gradient Boosting Decision Trees (GBDTs) and the interpretability of linear models. DEB constructs a high-fidelity linear feature space by synergizing Soft Binning, Polynomial Expansion, and Explicit Interaction Terms, optimized via a lightweight Bayesian engine. Extensive experiments demonstrate that DEB achieves predictive accuracy competitive with XGBoost while delivering distinct operational advantages: a 3x speedup in real-time inference on industrial datasets, an 8.8x increase in training throughput on large-scale synthetic benchmarks, and a 32x reduction in structural complexity compared to ensemble baselines. By reconciling nonlinear approximation with theoretical transparency and $O\left(N\right)$ scalability, DEB offers a robust solution for high-stakes, latency-sensitive industrial applications.