Active learning can effectively reduce the cost of labeling while enhancing model classification performance. However, prior studies have indicated that imbalanced class distributions adversely impact active learning, leading to diminished model effectiveness. Existing approaches to unbalanced active learning often neglect the multi-class imbalance problem and suffer from low performance and high time consumption. To address these issues, this paper introduces a hybrid active learning with online weighted broad learning system (HAL-OWBLS). Its main advantages include: (1) We optimize the initial labeled instance selection through an approximate query strategy to avoid the cold-start problem and introduce a sample selection strategy based on double uncertainty to enhance the rationality of active learning iterations. (2) A weighted broad learning system (WBLS) is chosen as the classifier, and an improved weighting strategy is adopted for multi-class imbalanced data. (3) We theoretically derive an efficient online updating model for WBLS, which reduces the time cost of active learning iterations by using only newly labeled samples for fast updating. The proposed HAL-OWBLS algorithm has better performance and robustness compared with existing related algorithms on various multi-class imbalanced data sets.

Active learning can significantly reduce the cost of labeling instances while improving model performance. However, similar to other traditional algorithms, active learning encounters the problem of class imbalance and delivers sub-optimal performance. Additionally, existing approaches suffer from poor performance and are time-consuming. To address these issues, we propose an Actively Incrementally Weighted Broad Learning System (AI-WBLS). Firstly, we introduce an active learning framework based on the weighted broad learning system, which employs a double uncertainty sample selection strategy to enhance the value and reasonableness of sample selection in each iteration of active learning. To further improve the model's adaptability during the iterative learning process, an adaptive weighting strategy is designed to adaptively modify the penalty weights according to the changes in the sample labels. Finally, an efficient incremental paradigm is developed to update the model with newly labelled samples instead of re-training, resulting in improved performance and efficiency. Extensive comparative experiments confirm that our approach outperforms other imbalanced active learning methods.

Broad learning system (BLS) is renowned for its excellent generalization and high efficiency in data classification. However, when confronted with class imbalance problems, BLS treats all samples as equally important, resulting in performance degradation. In addition, the presence of noise and outliers in imbalanced data further complicates BLS's ability to handle real-world classification problems. To address these challenges, this article proposes a graph-embedding intuitionistic fuzzy adaptive broad learning system (GEIB). The graph embedding strategy proposed by GEIB leverages the geometric topology of the data and class-specific information, effectively capturing variability among imbalanced samples and improving class separability. Furthermore, we introduce intuitionistic fuzzy theory. The BLS integrated with it considers both the homogeneity and heterogeneity of sample neighborhoods, enabling it to address uncertainty and imprecision in the data. It further differentiates clean samples from noisy ones in imbalanced datasets, thereby enhancing model robustness. To further investigate the prior distribution information of imbalanced data, we design an adaptive class-specific penalty mechanism based on global distribution and local density information. This mechanism accounts for both intraclass and interclass density information and class global distribution. We verify the superiority of our method by conducting a comparison with current approaches using real-world datasets that include both Gaussian noise and noise-free versions.

Imbalanced data poses a substantial challenge to conventional classification methods, which often disproportionately favor samples from the majority class. To mitigate this issue, various oversampling techniques have been deployed, but opportunities for optimizing data distributions remain underexplored. By exploiting the ability of metric learning to refine the sample distribution, we propose a novel approach, Imbalance Large Margin Nearest Neighbor (ILMNN). Initially, ILMNN is applied to establish a latent feature space, pulling intra-class samples closer and distancing inter-class samples, thereby amplifying the efficacy of oversampling techniques. Subsequently, we allocate varying weights to samples contingent upon their local distribution and relative class frequency, thereby equalizing contributions from minority and majority class samples. Lastly, we employ Kullback-Leibler (KL) divergence as a safeguard to maintain distributional similarity to the original dataset, mitigating severe intra-class imbalances. Comparative experiments on various class-imbalanced datasets verify that our ILMNN approach yields superior results.