Change detection in categorical evolving data streams

D. Ienco, A. Bifet, B. Pfahringer, P. Poncelet
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引用次数: 18

Abstract

Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution. To cope with these issues, we propose a new unsupervised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for categorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a descriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learning task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream.
分类演化数据流中的变化检测
检测不断发展的数据流中的变化是精确自适应学习的核心问题。在现实世界的应用中,数据流具有分类特征,而这些分类特征在数据分布中引起的变化迄今尚未得到广泛的考虑。以前关于变化检测的工作主要集中在检测学习器准确度的变化,而没有考虑数据分布的变化。为了解决这些问题,我们提出了一种新的无监督变化检测方法,称为CDCStream (change detection in Categorical Data Streams),它非常适合于分类数据流。所提出的方法能够检测批量增量场景中的变化。它基于以下两个特征:(i)提出了一种摘要策略,通过提取描述性摘要来压缩实际批处理;(ii)提出了一种新的分割算法,以突出显示数据流的变化并发出警告。为了评估我们的建议,我们将其应用于真实世界数据的学习任务中,并将其结果与最先进的方法进行比较。我们还报道了定性评价,以显示CDCStream的行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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