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Incremental Optimization Mechanism for Constructing a Balanced Very Fast Decision Tree for Big Data

  • Hang Yang
  • Simon Fong
Chapter

Abstract

Big data is a popular topic that highly attracts the attentions of researchers from all over the world. How to mine valuable information from such huge volumes of data remains an open problem. As the most widely used technology of decision tree, imperfect data stream leads to tree size explosion and detrimental accuracy problems. Over-fitting problem and the imbalanced class distribution reduce the performance of the original decision tree algorithm for stream mining. In this chapter, we propose an Optimized Very Fast Decision Tree (OVFDT) that possesses an optimized node-splitting control mechanism using Hoeffding bound. Accuracy, tree size, and 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. OVFDT operates incrementally by a test-then-train approach. Two new methods of functional tree leaves are proposed to 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 numeric and nominal datasets.

Notes

Acknowledgment

The authors are thankful for the financial support from the research grants “Temporal Data Stream Mining by Using Incrementally Optimized Very Fast Decision Forest (iOVFDF)”, Grant no. MYRG2015-00128-FST offered by the University of Macau, FST, and RDAO, and “A scalable data stream mining methodology: stream-based holistic analytics and reasoning in parallel”, Grant no. FDCT-126/2014/A3, offered by FDCT Macau.

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Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Hang Yang
    • 1
  • Simon Fong
    • 2
  1. 1.China Southern Power GridGuangzhouChina
  2. 2.Department of Computer and Information ScienceUniversity of Macau, Macau SARZhuhai ShiChina

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