An efficient and sensitive decision tree approach to mining concept-drifting data streams

Cheng Jung Tsai, Chien I. Lee, Wei Pang Yang

Research output: Contribution to journalArticle

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)135-156
Number of pages22
Issue number1
Publication statusPublished - 2008


All Science Journal Classification (ASJC) codes

  • Information Systems
  • Applied Mathematics

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