Semi-Supervised Ensemble Classifier Based on Distance Constraint for High-Dimensional Data

Guojie Li, Ziwei Fan, Zhiwen Yu, Kaixiang Yang, C. L. Philip Chen

Abstract: Due to its exceptional feature representation capabilities and high computational efficiency, the broad learning system (BLS) has been widely employed in various classification tasks. Nevertheless, BLS encounters considerable challenges in semi-supervised classification tasks involving complex heterogeneous data, given the data’s high-dimensional and noisy nature, coupled with a limited number of available labeled samples. To tackle these challenges, this article introduces a semi-supervised BLS based on distance constraint regularization (DRBLS) and a semi-supervised broad ensemble method (E-DRBLS) for high-dimensional data. Specifically, we present a distance constraint regularization (DR) that utilizes both labeled and unlabeled data to derive an optimal projection matrix, which maximizes the preservation of the original data’s intrinsic distribution structure. DR is designed to minimize intraclass distance, maximize interclass distance, and minimize the distance between neighboring samples. To boost the performance of BLS in semi-supervised classification, we integrate DR and BLS to construct the semi-supervised classifier DRBLS. Finally, we propose a mixed dimensionality reduction space generation (MDRSG) method that generates multiple high-quality and diverse mixed dimensionality reduction spaces (MDRSs). Based on MDRS, an ensemble framework, E-DRBLS, is developed for semi-supervised classification tasks targeting high-dimensional data. Comprehensive experiments confirm the superiority of the proposed methods.

External IDs:dblp:journals/tsmc/LiFYYC26