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INFORMATICALT
2008

An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams

8 years 4 months ago
An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams
Abstract. Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data block is basically a random sample from a stationary distribution, but many databases available violate this assumption. That is, the class of an instance may change over time, known as concept drift. In this paper, we propose a Sensitive Concept Drift Probing Decision Tree algorithm (SCRIPT), which is based on the statistical X2 test, to handle the concept drift problem on data streams. Compared with the proposed methods, the advantages of SCRIPT include: a) it can avoid unnecessary system cost for stable data streams; b) it can immediately and efficiently corrects original classifier while data streams are instable; c) it is more suitable to the applications in which a sensitive detection of concept drift is required. Key words: data mining, data streams, incremental learning, decision tree, concept drift.
Cheng-Jung Tsai, Chien-I Lee, Wei-Pang Yang
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2008
Where INFORMATICALT
Authors Cheng-Jung Tsai, Chien-I Lee, Wei-Pang Yang
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