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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Learning With Feature Evolvable Streams
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Learning With Feature Evolvable Streams

机译：学习功能可以进一步的流

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摘要

Learning with streaming data has attracted much attention during the past few years. Though most studies consider data stream with fixed features, in real practice the features may be evolvable. For example, features of data gathered by limited-lifespan sensors will change when these sensors are substituted by new ones. In this article, we propose a novel learning paradigm: Feature Evolvable Streaming Learning where old features would vanish and new features would occur. Rather than relying on only the current features, we attempt to recover the vanished features and exploit it to improve performance. Specifically, we learn a mapping from the overlapping period to recover old features and then we learn two models from the recovered features and the current features, respectively. To benefit from the recovered features, we develop two ensemble methods. In the first method, we combine the predictions from two models and theoretically show that with the assistance of old features, the performance on new features can be improved and we provide a tighter bound when the loss function is exponentially concave. In the second approach, we dynamically select the best single prediction and establish a better performance guarantee when the best model switches. Experiments on both synthetic and real data validate the effectiveness of our proposal.
机译：在过去的几年里，通过流媒体数据学习引起了很多关注。虽然大多数研究考虑使用固定特征的数据流，但在实际实践中，功能可以不可溶解。例如，当这些传感器被新的传感器代替时，由有限寿命传感器收集的数据的特征将改变。在本文中，我们提出了一种新颖的学习范式：特征可以进一步的流媒体学习，其中旧功能会消失，并且会发生新功能。我们尝试恢复消失的功能并利用它来提高性能，而不是仅依赖当前功能。具体而言，我们学习从重叠时段的映射来恢复旧功能，然后我们分别从恢复的功能和当前功能学习两个模型。要从恢复的功能中受益，我们开发了两个合奏方法。在第一种方法中，我们将预测从两个模型组合起来，从理论上表明，在旧功能的帮助下，可以提高新功能的性能，当损耗函数是指数凹的时，我们提供更严格的绑定。在第二种方法中，我们动态地选择最佳单一预测并在最佳型号交换机时建立更好的性能保证。合成和实数据的实验验证了我们提案的有效性。

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