Multi-Source Transfer Learning for Non-Stationary Environments
Multi-Source Transfer Learning for Non-Stationary Environments
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DOI：
10.1109/ijcnn.2019.8852024
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
Honghui Du;Leandro L. Minku;Huiyu Zhou
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文献类型：
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作者：
Honghui Du;Leandro L. Minku;Huiyu Zhou
在数据流挖掘中，由于概念漂移，预测模型的预测性能通常会下降。由于必须收集足够多的代表新概念的数据才能很好地学习新概念，现有模型的预测性能通常需要一段时间才能从概念漂移中恢复过来。为了提高数据流挖掘中概念漂移的恢复速度和预测性能，提出了一种非平稳环境下的多源在线迁移学习方法(Melanie)。Melanie是第一个能够在非平稳环境中的多个数据流来源之间传递知识的方法。它创建了几个子分类器，以随着时间的推移从不同的源和目标概念学习不同的方面。识别与当前目标概念匹配良好的子分类器，并将其用于组成用于从目标概念预测样本的集成。我们在几个包含不同类型概念漂移的合成数据流和真实世界数据流上对Melanie进行了评估。结果表明，Melanie能够处理多种漂移，并通过利用多个源来改善现有数据流学习算法的预测性能。
In data stream mining, predictive models typically suffer drops in predictive performance due to concept drift. As enough data representing the new concept must be collected for the new concept to be well learnt, the predictive performance of existing models usually takes some time to recover from concept drift. To speed up recovery from concept drift and improve predictive performance in data stream mining, this work proposes a novel approach called Multi-sourcE onLine TrAnsfer learning for Non-statIonary Environments (Melanie). Melanie is the first approach able to transfer knowledge between multiple data streaming sources in non-stationary environments. It creates several sub-classifiers to learn different aspects from different source and target concepts over time. The sub-classifiers that match the current target concept well are identified, and used to compose an ensemble for predicting examples from the target concept. We evaluate Melanie on several synthetic data streams containing different types of concept drift and on real world data streams. The results indicate that Melanie can deal with a variety drifts and improve predictive performance over existing data stream learning algorithms by making use of multiple sources.