TL;DR
The results demonstrate that CADEE can improve label utilization, drift adaptation, and minority class recognition in non-stationary multi-class imbalanced data streams.
This work proposes DMAE, a dual-memory active ensemble learning method for multiclass imbalanced concept-drifting data streams, and proposes a composite sample-weighting formulation, PCN-Weight, which jointly models boundary difficulty, class-imbalance status, sample–prototype relations, and temporal decay to guide inc...
The MLIDSC introduces an automated labeling mechanism that eliminates the need for prior parameter assumptions and uses a novel weighted scheme that combines the imbalance ratio and the importance of individual instances at a given time, ensuring a focus on critical data points.
Multi-class imbalanced classification remains difficult because minority classes can be poorly recognised even when aggregate performance appears acceptable. Many imbalance-handling methods still rely on a fixed technique, a single selected technique, or one level of adaptation, despite the fact that technique suitabil...
A data-driven semi-supervised framework for imbalanced binary image classification that does not depend on data augmentation, enabling reliable utilization of unlabeled data without introducing augmentation induced noise is introduced.
Audio classification is inherently a multi-label task, as real-world acoustic environments contain multiple simultaneous sound events. When new sound classes emerge, models must incorporate them without forgetting previously learned ones: a challenge known as class-incremental learning. Existing methods rely on storing...
With its lightweight design, HEDM-KD effectively mitigates the impact of class imbalance and demonstrates potential for improving detection robustness in resource-constrained scenarios.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
