• DocumentCode
    2757335
  • Title

    Mining Concept Drifts from Data Streams Based on Multi-Classifiers

  • Author

    Sun Yue ; Mao Guojun ; Liu Xu ; Liu Chunnian

  • Author_Institution
    Sch. of Comput. Sci., Beijing Univ. of Technol., Beijing
  • Volume
    2
  • fYear
    2007
  • fDate
    21-23 May 2007
  • Firstpage
    257
  • Lastpage
    263
  • Abstract
    Mining concept drifts is one of the most important fields in mining data streams. In this paper, a new ensemble algorithm called ICEA is proposed for mining concept drifts from data streams, which uses ensemble multi-classifiers to detect concept changes from the data streams in an incremental way. The experimental results show that ICEA algorithm performs higher accuracy and better adaptability than the popular methods such as SEA algorithm.
  • Keywords
    data analysis; data mining; data analysis; data mining; data streams; multi-classifiers; Change detection algorithms; Classification algorithms; Computer science; Data mining; Laboratories; Mobile computing; Partitioning algorithms; Streaming media; Sun; Telecommunication traffic;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Information Networking and Applications Workshops, 2007, AINAW '07. 21st International Conference on
  • Conference_Location
    Niagara Falls, Ont.
  • Print_ISBN
    978-0-7695-2847-2
  • Type

    conf

  • DOI
    10.1109/AINAW.2007.250
  • Filename
    4224114
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2757335