论文标题
分析漂流特征
Analysis of Drifting Features
论文作者
论文摘要
概念漂移的概念是指在观察到的数据基础的基本分布随时间变化的现象。我们对识别这些功能的识别感兴趣，这些功能与观察到的漂移最相关。我们区分了诱导漂移的特征，为此，观察到的特征漂移无法通过任何其他功能来解释，并忠实地漂移特征，这与其他功能的当前漂移相关。该概念产生了特征空间的最小亚集，可以表征整个观察到的漂移。我们将此问题与特征选择和功能相关性学习的问题联系起来，这使我们能够得出检测算法。我们证明了它在不同基准测试的有用性。
The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. We are interested in an identification of those features, that are most relevant for the observed drift. We distinguish between drift inducing features, for which the observed feature drift cannot be explained by any other feature, and faithfully drifting features, which correlate with the present drift of other features. This notion gives rise to minimal subsets of the feature space, which are able to characterize the observed drift as a whole. We relate this problem to the problems of feature selection and feature relevance learning, which allows us to derive a detection algorithm. We demonstrate its usefulness on different benchmarks.
