MGL-FBLS: Multi-Granularity Label-Driven Feature Enhanced Broad Learning System for Semi-Supervised Classification

The Broad Learning System (BLS) performs well in semi-supervised scenarios across many datasets, but its effectiveness is often limited by inadequate feature extraction and inefficient label utilization. To overcome these issues, we propose a Multi-Granularity Label-Driven Feature Enhanced Broad Learning System (MGL-FBLS) for Semi-Supervised Classification in this paper. First, for samples' feature enhancement, Random Fourier Features are imposed on the traditional broad model, which further strengthens the system's ability to capture nonlinear structures, without destroying the elegant and efficient randomness of BLS. It is worth noting that the model at this point can use exactly the same optimization method as the original model to handle fully supervised classification problems with higher accuracy efficiently. From the perspective of labels, our approach employs a multi-granularity labeling strategy that fully leverages both coarse and fine-granularity label information, enhancing the model's interpretability for unsupervised samples. In addition, a multi-manifold term is designed to describe the coarse-granularity semi-supervised information accurately and embed it into the sample feature-enhanced network efficiently. Meanwhile, a low-complexity, fast-converging algorithm is theoretically validated to maximize the use of both labeled and unlabeled data during training. Finally, comparative experiments in four benchmarks validate the proposed framework's effectiveness, demonstrating its competitive advantages over state-of-the-art methods.