Data Mining and Knowledge Discovery

, Volume 29, Issue 6, pp 1622–1664 | Cite as

A general framework for never-ending learning from time series streams

  • Yanping Chen
  • Yuan Hao
  • Thanawin Rakthanmanon
  • Jesin Zakaria
  • Bing Hu
  • Eamonn Keogh
Article
  • 596 Downloads

Abstract

Time series classification has been an active area of research in the data mining community for over a decade, and significant progress has been made in the tractability and accuracy of learning. However, virtually all work assumes a one-time training session in which labeled examples of all the concepts to be learned are provided. This assumption may be valid in a handful of situations, but it does not hold in most medical and scientific applications where we initially may have only the vaguest understanding of what concepts can be learned. Based on this observation, we propose a never-ending learning framework for time series in which an agent examines an unbounded stream of data and occasionally asks a teacher (which may be a human or an algorithm) for a label. We demonstrate the utility of our ideas with experiments that consider real-world problems in domains as diverse as medicine, entomology, wildlife monitoring, and human behavior analyses.

Keywords

Never-ending learning Classification Data streams Time series 

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Copyright information

© The Author(s) 2014

Authors and Affiliations

  • Yanping Chen
    • 1
  • Yuan Hao
    • 1
  • Thanawin Rakthanmanon
    • 2
  • Jesin Zakaria
    • 1
  • Bing Hu
    • 1
  • Eamonn Keogh
    • 1
  1. 1.Department of Computer Science & EngineeringUniversity of California, RiversideRiversideUSA
  2. 2.Department of Computer Science & EngineeringKasetsart UniversityBangkokThailand

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