多源迁移学习解决动态环境中的概念漂移问题
MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments
- 将目标概念映射到各源空间，实现多源知识融合
- 在源目标概念不匹配时仍保持更高预测准确率
- 适合实时数据流中概念频繁变化的场景
概念漂移是在线学习中的重大挑战，会显著降低数据流挖掘系统的预测性能。近期研究尝试利用不同来源的数据流来应对特定目标域的概念漂移，但这类方法通常假设至少一个源模型与目标概念相似，这一假设在真实场景中常不成立。本文提出一种新方法MARLINE（Multi-source mApping with tRansfer LearnIng for Non-stationary Environments），可在非平稳环境中从多个数据源获取知识，即使源与目标概念不一致也能有效提升性能。该方法通过将目标概念投影至每个源概念空间，使多个源子分类器共同参与目标预测，构成集成模型。在多个合成与真实世界数据集上的实验表明，MARLINE在准确性上优于多种前沿数据流学习方法。
原文摘要 · Abstract (English)
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.
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