Abstract: In high-dimensional small sample classification tasks,BLS (Broad Learning System) has garnered much attention due to its efficiency.However,the feature extraction capability of the single-layer BLS is limited,making it difficult to handle complex high-dimensional data.The random node generation mechanism induces node redundancy when directly stacking BLS hidden la-yers,thereby hindering improvements in model performance.To address these issues,an ensemble stack BLS(E-SBLS) algorithm is proposed.E-SBLS utilizes the output of the previous BLS layer as enhanced features,concatenates them with the original feature weighted by classification confidence,and sends them into the subsequent BLS to continuously enhance the feature representation capability in deeper layers.By integrating the outputs of multiple BLS layers through a meta-learner pool,the high-dimensional feature extraction ability of the original single-layer BLS is augmented,thereby improving the generalizationperfor-mance of the proposed model.Furthermore,considering the complex and variable characteristics of high-dimensional data,a dynamic ensemble framework is designed to adjust the complexity of the model dynamically based on data difficulties.The proposed method further enhances ensemble efficiency while maintaining model performance.Ablation experiments validate the effectiveness of each module in the proposed algorithm,and comparative experiments demonstrate the superior classification performance of the proposed model on high-dimensional disease data.

Key words: Broad learning system, Ensemble learning, Dynamic structure, High-dimensional data, Stacking