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Parallel Hoeffding Decision Tree for Streaming Data

  • Piotr CalEmail author
  • Michał Woźniak
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 217)

Abstract

Decision trees are well known, widely used algorithm for building efficient classifiers.We propose the modification of the Parallel Hoeffding Tree algorithm that could deal with large streaming data. The proposed method were evaluated on the basis of computer experiment which were carried on few real datasets. The algorithm uses parallel approach and the Hoeffding inequality for better performance with large streaming data. The paper present the analysis of Hoeffding tree and its issues.

Keywords

machine learning supervised learning decision tree parallel decision tree pattern recognition 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWrocław University of TechnologyWrocławPoland

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