Reliable Early Classification on Multivariate Time Series with Numerical and Categorical Attributes

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• 作者：Yu-Feng Lin (10)
  Hsuan-Hsu Chen (10)
  Vincent S. Tseng (11)
  Jian Pei (12)
  
  10. Department of Computer Science and Information Engineering ; National Cheng Kung University ; Tainan ; Taiwan ; Republic of China
  11. Department of Computer Science ; National Chiao Tung University ; Hsinchu ; Taiwan ; Republic of China
  12. School of Computing Science ; Simon Fraser University Burnaby ; Burnaby ; BC ; Canada
  
• 关键词：Early classification ; Multivariate time series ; Serial classifier ; Numerical and categorical attributes ; Shapelets ; GPU
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9077
• 期：1
• 页码：199-211
• 全文大小：326 KB
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• 作者单位：Advances in Knowledge Discovery and Data Mining
• 丛书名：978-3-319-18037-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Early classification on multivariate time series has recently emerged as a novel and important topic in data mining fields with wide applications such as early detection of diseases in healthcare domains. Most of the existing studies on this topic focused only on univariate time series, while some very recent works exploring multivariate time series considered only numerical attributes and are not applicable to multivariate time series containing both of numerical and categorical attributes. In this paper, we present a novel methodology named REACT (Reliable EArly ClassificaTion), which is the first work addressing the issue of constructing an effective classifier on multivariate time series with numerical and categorical attributes in serial manner so as to guarantee stability of accuracy compared to the classifiers using full-length time series. Furthermore, we also employ the GPU parallel computing technique to develop an extended mechanism for building the early classifier efficiently. Experimental results on real datasets show that REACT significantly outperforms the state-of-the-art method in terms of accuracy and earliness, and the GPU implementation is verified to substantially enhance the efficiency by several orders of magnitudes.