An Efficient Classification System Based on Binary Search Trees for Data Streams Mining

Authors:
Tao Wang;Zhoujun Li;Yuejin Yan;Huowang Chen;Jinshan Yu
Affiliations:
National University of Defense Technology, Changsha, 410073, China;Beihang University, Beijing, 100083, China;National University of Defense Technology, Changsha, 410073, China;National University of Defense Technology, Changsha, 410073, China;National University of Defense Technology, Changsha, 410073, China
Venue:
ICONS '07 Proceedings of the Second International Conference on Systems
Year:
2007

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An Incremental Fuzzy Decision Tree Classification Method for Mining Data Streams

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A new fuzzy decision tree classification method for mining high-speed data streams based on binary search trees

FAW'07 Proceedings of the 1st annual international conference on Frontiers in algorithmics
A new decision tree classification method for mining high-speed data streams based on threaded binary search trees

PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Increasing availability of industrial systems through data stream mining

Computers and Industrial Engineering

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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.