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TRACKING RECURRENT CONCEPT DRIFT IN STREAMING DATA USING ENSEMBLE CLASSIFIERS

RAMAMURTHY, SASTHAKUMAR

Abstract Details

2007, MS, University of Cincinnati, Engineering : Computer Science.
Streaming data may consist of multiple drifting concepts each having its own under- lying data distribution. We present an ensemble learning based approach to handle the data streams having multiple underlying modes. We build a global set of clas- sifiers from sequential data chunks; ensembles are then selected from this global setof classifiers, and new classifiers created if needed, to represent the current conceptin the stream. The system is capable of performing any-time classification and todetect concept drift in the stream. In streaming data historic concepts are likely to reappear so we dont delete any of the historic classifiers. Instead, we judiciously select only pertinent classifiers from the global set while forming the ensemble set for a classification task.
Dr. Raj Bhatnagar (Advisor)
58 p.

Recommended Citations

Citations

  • RAMAMURTHY, S. (2007). TRACKING RECURRENT CONCEPT DRIFT IN STREAMING DATA USING ENSEMBLE CLASSIFIERS [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196103577

    APA Style (7th edition)

  • RAMAMURTHY, SASTHAKUMAR. TRACKING RECURRENT CONCEPT DRIFT IN STREAMING DATA USING ENSEMBLE CLASSIFIERS. 2007. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196103577.

    MLA Style (8th edition)

  • RAMAMURTHY, SASTHAKUMAR. "TRACKING RECURRENT CONCEPT DRIFT IN STREAMING DATA USING ENSEMBLE CLASSIFIERS." Master's thesis, University of Cincinnati, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196103577

    Chicago Manual of Style (17th edition)