Title page for etd-0725114-180851


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URN etd-0725114-180851
Author Che-jui Hsu
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2014
Semester 1
Degree Master
Type of Document
Language English
Title Flexible Dynamic Time Warping for Time Series Classification
Date of Defense 2014-08-19
Page Count 49
Keyword
  • Time Series Classification
  • Dynamic Programming
  • Behavior Knowledge Space
  • Dominant Strategy
  • Dynamic Time Warping
  • Flexible Dynamic Time Warping
  • Abstract Measuring the similarity or distance between two time series sequences is critical for the classification of a set of time series sequences. Given two time series sequences, X and Y, the dynamic time warping (DTW) algorithm can calculate the distance between X and Y. But the DTW algorithm may align some neighboring points in X to the corresponding points which are far apart in Y. This situation may cause that the alignment gets only a high alignment score, but it may lose its representative information. In this thesis, we propose the flexible dynamic time wrapping (FDTW) method for measuring the similarity of two time series sequences. Our algorithm adds an additional score as the reward for the long contiguous one-to-one segment. We also present the voting schemes and the behavior knowledge space (BKS) methods to construct classifier ensembles. As the experimental results show, our FDTW is indeed a crucial factor for improving the classification accuracy. The performance of a classifier ensemble, built by either voting or BKS, outperforms a single method.
    Advisory Committee
  • Shih-Chung Chen - chair
  • Yung-Hsing Peng - co-chair
  • Chien-Feng Huang - co-chair
  • Chang-Biau Yang - advisor
  • Files
  • etd-0725114-180851.pdf
  • Indicate in-campus at 0 year and off-campus access at 1 year.
    Date of Submission 2014-08-25

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