Incremental learning of variable rate concept drift

Ryan Elwell, Robi Polikar

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    26 Scopus citations

    Abstract

    We have recently introduced an incremental learning algorithm, Learn ++.NSE, for Non-Stationary Environments, where the data distribution changes over time due to concept drift. Learn+ +.NSE is an ensemble of classifiers approach, training a new classifier on each consecutive batch of data that become available, and combining them through an age-adjusted dynamic error based weighted majority voting. Prior work has shown the algorithm's ability to track gradually changing environments as well as its ability to retain former knowledge in cases of cyclical or recurring data by retaining and appropriately weighting all classifiers generated thus far. In this contribution, we extend the analysis of the algorithm to more challenging environments experiencing varying drift rates; but more importantly we present preliminary results on the ability of the algorithm to accommodate addition or subtraction of classes over time. Furthermore, we also present comparative results of a variation of the algorithm that employs an error-based pruning in cyclical environments.

    Original languageEnglish (US)
    Title of host publicationMultiple Classifier Systems - 8th International Workshop, MCS 2009, Proceedings
    Pages142-151
    Number of pages10
    DOIs
    StatePublished - 2009
    Event8th International Workshop on Multiple Classifier Systems, MCS 2009 - Reykjavik, Iceland
    Duration: Jun 10 2009Jun 12 2009

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume5519 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other8th International Workshop on Multiple Classifier Systems, MCS 2009
    Country/TerritoryIceland
    CityReykjavik
    Period6/10/096/12/09

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)

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