应对数据流中概念漂移与类别不平衡的增量学习方法
Resilient Class-Incremental Learning: on the Interplay of Drifting, Unlabelled and Imbalanced Data Streams
- 结合自编码器与多层感知机，双损失驱动预测与新类检测
- 使用修正伪标签和队列机制，有效缓解类别不平衡与概念漂移
- 适用于真实场景中的持续学习，尤其适合资源受限环境
在当今互联世界中，跨多个领域的海量数据流已成常态。面对概念漂移、类别不平衡、标签稀缺及新类别出现，现有方法易导致表征不稳定、学习偏向旧分布，降低动态环境下的检测可靠性。本文提出SCIL（Streaming Class-Incremental Learning）框架，融合自编码器（AE）与多层感知机实现多分类预测，采用分类与重建双损失策略以支持预测与新类发现，利用修正伪标签进行在线训练，通过队列管理类别，结合过采样处理不平衡问题。通过消融实验阐明方法设计原理，并在包含类别不平衡、增量类别和概念漂移的真实与合成数据集上进行全面评估。结果表明，SCIL显著优于多个强基线与前沿方法。我们坚持开放科学原则，已公开代码与数据集。
原文摘要 · Abstract (English)
In today's connected world, the generation of massive streaming data across diverse domains has become commonplace. In the presence of concept drift, class imbalance, label scarcity, and new class emergence, they jointly degrade representation stability, bias learning toward outdated distributions, and reduce the resilience and reliability of detection in dynamic environments. This paper proposes SCIL (Streaming Class-Incremental Learning) to address these challenges. The SCIL framework integrates an autoencoder (AE) with a multi-layer perceptron for multi-class prediction, uses a dual-loss strategy (classification and reconstruction) for prediction and new class detection, employs corrected pseudo-labels for online training, manages classes with queues, and applies oversampling to handle imbalance. The rationale behind the method's structure is elucidated through ablation studies and a comprehensive experimental evaluation is performed using both real-world and synthetic datasets that feature class imbalance, incremental classes, and concept drifts. Our results demonstrate that SCIL outperforms strong baselines and state-of-the-art methods. Based on our commitment to Open Science, we make our code and datasets available to the community.
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