Abstract
Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data streams from different sources as a strategy to tackle concept drift in a given target domain. These approaches make the assumption that at least one of the source models represents a concept similar to the target concept, which may not hold in many real-world scenarios. In this paper, we propose a novel approach called Multi-source mApping with tRansfer LearnIng for Non-stationary Environments (MARLINE). MARLINE can benefit from knowledge from multiple data sources in non-stationary environments even when source and target concepts do not match. This is achieved by projecting the target concept to the space of each source concept, enabling multiple source sub-classifiers to contribute towards the prediction of the target concept as part of an ensemble. Experiments on several synthetic and real-world datasets show that MARLINE was more accurate than several state-of-the-art data stream learning approaches.
Abstract (translated)
概念漂移是在线学习中的一个主要问题，因为它会影响数据流挖掘系统的预测性能。最近的研究已经开始探索来自不同来源的数据流作为解决特定目标领域中概念漂移的策略。这些方法假设至少有一个源模型代表了与目标概念相似的概念，但这一假设在许多现实场景中可能并不成立。本文提出了一种名为多源映射和迁移学习用于非平稳环境（MARLINE）的新方法。即使当源概念与目标概念不匹配时，MARLINE也能从多个数据源的知识中受益，在非平稳环境中发挥作用。这是通过将目标概念投影到每个源概念的空间来实现的，从而使多个源子分类器能够作为集成的一部分共同预测目标概念。在几个合成和现实世界的数据集上的实验表明，MARLINE比几种最新的数据流学习方法更准确。
URL
https://arxiv.org/abs/2509.08176
