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Decision tree, as one of the most widely used methods in data mining, has been used in many realistic application. Incremental decision tree handles streaming data scenario that is applicable for big data analysis. However, imperfect data are unavoidable in real-world applications. Studying the state-of-art incremental decision tree induction using Hoeffding bound, we investigated the influence of...
Hoeffding's bound (HB) has been widely used for node splitting in incremental decision tree algorithms. Many decision-tree algorithms adopt a sliding-window technique to detect concept drift when mining changing data streams. This paper presents a novel node-splitting approach that replaces the traditional HB with a new measure. The new measure is derived from a loss function applied in a cache-based...
How to efficiently uncover the knowledge hidden within massive and big data remains an open problem. One of the challenges is the issue of 'concept drift' in streaming data flows. Concept drift is a well-known problem in data analytics, in which the statistical properties of the attributes and their target classes shift over time, making the trained model less accurate. Many methods have been proposed...
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