The broad learning system (BLS) is a lightweight neural network known for its efficient learning capabilities; however, it is limited by its reliance on a binary label strategy. Existing label enhancement models primarily focus on increasing the distances between labels from different classes, which inadvertently expands the distance within the same category. For classification tasks, maintaining similarity within the intraclass is essential for ensuring the model's effectiveness. To address this issue, we propose a groupwise label enhancement BLS model that ensures both intraclass similarity and interclass disparity of labels. Specifically, we develop a novel regression target that generalizes existing label enhancement targets in BLS, increasing the distances between labels of different classes while overcoming the constraints imposed by binary labels. Moreover, we design a groupwise constraint to jointly enhance the intraclass similarity and interclass disparity of labels. Additionally, we propose a novel alternating direction method of multipliers-based optimization algorithm to solve our proposed model, ensuring both computational efficiency and theoretical convergence. Experimental results on several public datasets demonstrate the outstanding effectiveness and efficiency of our proposed model compared to other state-of-the-art methods.

Fuzzy Broad Learning System (Fuzzy BLS) constitutes an effective neural network architecture that has demonstrated remarkable efficacy across various real-world application domains. Nonetheless, Fuzzy BLS may result in suboptimal performance and face challenges in effectively addressing the issue of imbalanced classification. To tackle the challenge mentioned above, a novel Mixture-of-Expert-based Fuzzy Broad Learning System (MFBLS) is proposed. In MFBLS, a fuzzy system is integrated to deal with the fuzziness of input data. Concurrently, based on the advantages of the Mixture-of-expert framework, the feature weight of each expert system is dynamically adjusted via a gating network to enhance the importance of key features, thereby enhancing the overall capability of the model. Besides, several classical over-sampling techniques are employed to address sample imbalance in datasets to achieve a more balanced class distribution. Subsequently, the Extreme Gradient Boosting (XGBoost) algorithm is utilized on the oversampled dataset for feature selection, aiming to enhance the efficiency and the predictive precision of subsequent model training in MFBLS. Finally, through a comparative analysis of state-of-the-art machine learning methods, the superiority of MFBLS in handling imbalanced datasets is validated through a series of experimental evaluations on various imbalanced datasets.

As an effective alternative to deep neural networks, broad learning system (BLS) has attracted more attention due to its efficient and outstanding performance and shorter training process in classification and regression tasks. Nevertheless, the performance of BLS will not continue to increase, but even decrease, as the number of nodes reaches the saturation point and continues to increase. In addition, the previous research on neural networks usually ignored the reason for the good generalization of neural networks. To solve these problems, this article first proposes the Extreme Fuzzy BLS (E-FBLS), a novel cascaded fuzzy BLS, in which multiple fuzzy BLS blocks are grouped or cascaded together. Moreover, the original data is input to each FBLS block rather than the previous blocks. In addition, we use residual learning to illustrate the effectiveness of E-FBLS. From the frequency domain perspective, we also discover the existence of the frequency principle in E-FBLS, which can provide good interpretability for the generalization of the neural network. Experimental results on classical classification and regression datasets show that the accuracy of the proposed E-FBLS is superior to traditional BLS in handling classification and regression tasks. The accuracy improves when the number of blocks increases to some extent. Moreover, we verify the frequency principle of E-FBLS that E-FBLS can obtain the low-frequency components quickly, while the high-frequency components are gradually adjusted as the number of FBLS blocks increases.

Epilepsy as the most common neurological disorder globally has drawn more and more attention. However, it is time-consuming and labor-intensive for manual detection of interictal epileptic discharges (IEDs). Thus, there is an urgent need to develop an efficient and automated detection approach as a more accurate diagnostic alternative for epilepsy detection. Recently, fuzzy broad learning system (FBLS) has been recognized as an alternative to deep learning and utilized in various fields. Nevertheless, FBLS ignores the locally invariant property of data. To effectively address this issue and further improve the performance of FBLS, a novel graph-regularized fuzzy broad learning system (GFBLS) is first proposed based on graph regularization . Moreover, an automated GFBLS-based approach is proposed for IEDs detection from EEG recordings. In the proposed method, graph convolutional neural networks (GCN) is firstly utilized to extract features from line graphs with undirected connections, which are constructed by EEG recordings, then extracted features by GCN are fed into GFBLS for IEDs detection. The experimental results demonstrated that GFBLS can achieve accuracy of 92.20%, specificity of 90.90% and precision of 91.13% with the training time of only 31.6 s, which is superior or comparable performance compared with other state-of-the-art approaches.

The Scalp Electroencephalogram (EEG) signal of epileptic patients often contains Interictal Epileptiform Discharges (IED) during the period of seizures. Detection of IEDs is significant for the diagnosis of epilepsy and the prediction of seizures. In this paper, we proposed a graph convolutional network and bi-directional LSTM co-embedded broad learning system to detect IEDS. Here, we represent EEG signal as a graph and utilize Graph Convolutional Networks (GCN) to extract contextual features. In addition, bi-directional LSTM is also adopted for extracting the temporal feature from signals. Then these features are incorporated into Broad Learning System (BLS) to automatically detect epileptiform events. Experimental results indicate the proposed approach can achieve superior accuracy in the classification of IEDs than other commonly used time series processing models and reach a consensus with neurologists in predicting the lead of an EEG recording.

Region-based image fusion methods have a number of advantages over pixel-based image fusion methods. In this paper, we propose a region-based multi-focus image fusion approach using guided filtering and greedy analysis. The original images are enhanced by guided filter first and then we conduct the sparse representation of images using the greedy algorithm. Here, simultaneously orthogonal matching pursuit (SOMP) algorithm is adopted, which could obtain more accurate sparse coefficients under the same basis by processing the source image simultaneously. In order to form the regional map, the clarity enhanced image is designed and normalized cuts algorithm is adopted to segment it. According to the regional fused sparse coefficients, we recover the fused image. To verify the effectiveness of the proposed method, several pairs of multi-focus images are tested. Comparing with other fusion methods, the experiment results demonstrate that the performance of multifocus image fusion by our proposed method is superior.