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Mathematical Problems in Engineering
Volume 2013, Article ID 580397, 14 pages
Research Article

Incremental Optimization Mechanism for Constructing a Decision Tree in Data Stream Mining

Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau

Received 21 September 2012; Revised 21 January 2013; Accepted 29 January 2013

Academic Editor: Sabrina Senatore

Copyright © 2013 Hang Yang and Simon Fong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Imperfect data stream leads to tree size explosion and detrimental accuracy problems. Overfitting problem and the imbalanced class distribution reduce the performance of the original decision-tree algorithm for stream mining. In this paper, we propose an incremental optimization mechanism to solve these problems. The mechanism is called Optimized Very Fast Decision Tree (OVFDT) that possesses an optimized node-splitting control mechanism. Accuracy, tree size, and the learning time are the significant factors influencing the algorithm’s performance. Naturally a bigger tree size takes longer computation time. OVFDT is a pioneer model equipped with an incremental optimization mechanism that seeks for a balance between accuracy and tree size for data stream mining. It operates incrementally by a test-then-train approach. Three types of functional tree leaves improve the accuracy with which the tree model makes a prediction for a new data stream in the testing phase. The optimized node-splitting mechanism controls the tree model growth in the training phase. The experiment shows that OVFDT obtains an optimal tree structure in both numeric and nominal datasets.