Batch Weight-Averaged Broad Learning System for Improved Generalization

Abstract

The Broad Learning System (BLS) enables efficient closed-form training by solving the output weights in a single ridge-regression step. However, this one-shot full-batch estimator often exhibits a pronounced generalization gap. This paper proposes Batch Weight-Averaged BLS (BWA-BLS), which partitions the training set into K non-overlapping batches, computes an independent ridge estimator for each batch, and averages the resulting output weight matrices. The proposed method preserves the non-iterative BLS pipeline while introducing a lightweight consensus mechanism that stabilizes output-layer estimation. Experiments on multiple benchmark datasets, with results averaged over multiple independent runs, show that the BWA-BLS outperforms the standard BLS and remains competitive with a Bagging-BLS baseline while substantially reducing the generalization gap. A K-perturbation study further indicates that moderate values of K provide the most favorable balance between predictive accuracy and computational cost. These results demonstrate that the BWA-BLS provides an effective and practical enhancement to the standard BLS for improved generalization.