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Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system (MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system (MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.

In the current era of big data, securing computer networks and preventing cyberattacks have become a major challenge. In this regard, we present a novel approach for addressing the imbalanced problem of network traffic data. Specifically, the proposed method MC-OCBLS, leverages the maximum correntropy criterion (MCC) to develop a one-class broad learning system. The robustness of the one-class classification model is enhanced by maximizing the correlation entropy. Moreover, considering the limited generalization capability of individual models, we propose the boosting ensemble model SMC-OCBLS, which assigns higher weights to misclassified samples and classifiers with lower error rates. To increase the variety of classifiers in the ensemble framework, feature columns are randomly disrupted during training to construct different feature spaces. Training multiple hybrid classifiers using various feature spaces further enhances the accuracy and generalizability of the model. The experimental results demonstrate the superiority of the proposed model over other advanced approaches.

Smart pipeline systems (SPSs) based on phase-sensitive optical time-domain reflectometry ( $\phi $ -OTDR) distributed optical fiber sensors (DOFSs) are widely used to recognize and locate third-party events that may damage long-distance pipelines. However, the geological surroundings and infrastructures along the pipeline are highly complex, encompassing densely populated highways, mountains, railways, and so on, and thus, the vibration intensities also vary greatly. Moreover, the system in real sites always entails high computation costs, leading to lengthy delays that cannot meet real-time demands. In this article, a data-driven two-stage early warning strategy is designed to monitor the safety of oil and gas pipelines. Specifically, the quality of classification model can be greatly enhanced by identifying and excising redundant values through a preselection mechanism. In addition, the study proposes a model based on cascade forest to recognize events. Extensive comparative experiments on real-world datasets demonstrate the superiority of the proposed methods in terms of both effectiveness and efficiency.

Broad learning system (BLS) is a simple yet efficient learning algorithm that only needs to train a three-layer feedforward neural network. Although various BLS variants have been designed for supervised learning, none have been used for unsupervised learning. This paper proposes BLS-AE, a novel data clustering scheme that seamlessly combines BLS and auto-encoder. Then, graph regularization is introduced into BLS-AE to increase the capability of learning intrinsic structures in data and adaptation to various data simultaneously, which is termed BLSg-AE. Moreover, different concatenation styles of feature and enhancement nodes are investigated for reusing the learned features, followed by designing two special strategies (i.e., pruning optimization and incremental learning) to reduce the parameter scale significantly and improve performance, which is termed xBLSg-AE. To address the performance instability issue caused by random subspace in a single xBLSg-AE, the x-cascade broad learning system graph regularization multi-auto-encoder (xBLSg-MAE) algorithm is proposed. Extensive experiments are conducted on multiple real data sets to demonstrate that the proposed methods are more effective and robust than competing approaches.

Ensemble clustering has an advantage in producing a more promising and robust clustering result by combining multiple partitions strategically. The quality of both base partitions and co-association matrix plays an essential role in improving the consensus partition. However, the current ensemble clustering methods have several limitations: 1) The noise in high-dimensional feature space is ignored; 2) The independent base partition generation process does not pay attention to ambiguous samples; 3) The co-association matrix and the weights of base partitions commonly lack of theoretical optimization. In order to address these issues, we propose an adaptive ensemble clustering framework with boosting BLS-based autoencoder (BoostAEC). In the generation step, a boosting BLS-based autoencoder (BoostBLSAE) is designed to generate base partitions sequentially, which learns compressed feature subspaces for ambiguous samples and adaptively evaluates the corresponding weights of reliability. In the integration step, we construct a fuzzy membership function to capture the inter-cluster correlation and explicitly propose a consensus objective function to optimize the unified co-association matrix by considering the weighted base partitions. Extensive experiments on various real-world datasets demonstrate the superior performance of BoostAEC to the state-of-the-art ensemble clustering methods.