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Information Technology Journal
  Year: 2013 | Volume: 12 | Issue: 24 | Page No.: 8469-8475
DOI: 10.3923/itj.2013.8469.8475
Learning Decision Trees from Time-changing Uncertain Data Streams
Chunquan Liang, Yang Zhang and Shaojun Hu

Abstract:
In this study, we study the problem of classifying uncertain data streams. Based on CVFDT algorithm, we proposed a novel algorithm, namely uCVFDTc, to learn very fast decision trees from uncertain data streams with concept drift. In training phase, the uCVFDTc algorithm uses Hoeffding bound theory to yield fast and reasonable decision trees. In classification phase, at tree leaves it uses Uncertain Naive Bayes (UNB) classifiers to improve classification performance. Experimental results showed that uCVFDTc had strong ability to learn from uncertain data streams and cope with concept drift; the use of UNB at tree leaves had improved the performance of uCVFDTc, especially the ability to handle concept drift.
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How to cite this article:

Chunquan Liang, Yang Zhang and Shaojun Hu, 2013. Learning Decision Trees from Time-changing Uncertain Data Streams. Information Technology Journal, 12: 8469-8475.

DOI: 10.3923/itj.2013.8469.8475

URL: https://scialert.net/abstract/?doi=itj.2013.8469.8475

 
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