摘要
在高维小样本分类任务中,宽度学习系统(Broad Learning System,BLS)因其高效的特性而备受关注。然而,原始的单层BLS的特征提取能力有限,难以处理复杂的高维数据。随机节点生成机制导致直接堆叠BLS隐层时出现节点冗余,模型性能难以提升。为解决上述问题,提出了一种集成堆叠BLS算法。所提算法利用前一层BLS的输出作为增强特征,将其与按分类置信度加权的原始特征进行拼接后输入下一层BLS,不断提高深层特征表达能力。通过元学习器池集成多个BLS层的输出,增强了原始单层BLS的高维特征提取能力,从而提升了模型的泛化性能。此外,考虑到高维数据复杂多变的特性,设计了动态集成框架,根据数据难度动态调整模型的复杂度。所提方法在保持模型性能的同时,进一步提升了集成效率。消融实验证明了所提算法的各个模块的有效性,对比实验证明了所提算法在高维疾病数据上的优越分类性能。
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 generalization perfor-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.
作者
云帆
余志文
杨楷翔
YUN Fan;YU Zhiwen;YANG Kaixiang(College of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,China)
出处
《计算机科学》
北大核心
2026年第4期48-56,共9页
Computer Science
基金
国家自然科学基金(62572199,92467109,U21A20478)
国家重点研发计划(2023YFA1011601)。
关键词
宽度学习系统
集成学习
动态结构
高维数据
堆叠
Broad learning system
Ensemble learning
Dynamic structure
High-dimensional data
Stacking
