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An efficient classification system based on binary search trees for data streams mining

  • Tao Wang*
  • , Zhoujun Li
  • , Yuejin Yan
  • , Huowang Chen
  • , Jinshan Yu
  • *Corresponding author for this work
  • National University of Defense Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Decision tree construction is a well-studied problem in data mining. Recently, there has been much interest in mining data streams. Domingos and Hulten have presented a one-pass algorithm for decision tree constructions. Their system using Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed. In this paper, we revisit this problem and propose a decision tree classifier system that uses binary search trees to handle numerical attributes. The proposed system is based on the most successful VFDT, and it achieves excellent performance. The most relevant property of our system is an average large reduction in processing time, while keeps the same tree size and accuracy.

Original languageEnglish
Title of host publication2nd International Conference on Systems, ICONS 2007
PublisherIEEE Computer Society
Pages15-20
Number of pages6
ISBN (Print)0769528074, 9780769528076
DOIs
StatePublished - 2007
Event2nd International Conference on Systems, ICONS 2007 - Sainte-Luce, Martinique, France
Duration: 22 Apr 200728 Apr 2007

Publication series

Name2nd International Conference on Systems, ICONS 2007

Conference

Conference2nd International Conference on Systems, ICONS 2007
Country/TerritoryFrance
CitySainte-Luce, Martinique
Period22/04/0728/04/07

Keywords

  • Binary search tree
  • Data streams
  • Numerical attributes
  • VFDT

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