JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2011, Vol. 41 ›› Issue (4): 73-78.

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Ensemble learning of multi-classifier for early classification of time series

LI Xiao-bin1, LI Shi-yin2   

  1. 1. School of Computer Science and Technology, Xuzhou Normal University, Xuzhou 221116, China;
    2. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221008, China
  • Received:2011-02-20 Online:2011-08-16 Published:2011-02-20

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Abstract:

 To solve the early classification for time series in some time-sensitive application area, an ensemble learning method named sequential subspace stacked generation (SSSG) was introduced. This method split time series into several sequential subspaces with slider windows. Multi first-layer classifiers were used on these sequential subspaces and label probability for these subspaces was generated. Then these probability results were input for the second layer classifier. The time series’ label could be predicated by the two-layer classifier. Experiment results showed that this method could both do early classification for time series and achieve higher classification accuracy than only one classifier.

Key words: time series, ensemble learning, classification, sequential subspace, stacked generation

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