Comparison of ensemble techniques for incremental learning of new concept classes under hostile non-stationary environments

Hussein Syed Mohammed, James Leander, Matthew Marbach, Robi Polikar

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

    3 Scopus citations

    Abstract

    We have recently introduced Learn++, an incremental learning algorithm, inspired by the multiple classifiers structure of AdaBoost. Both algorithms generate an ensemble of classifiers trained on bootstrapped replicates of the training data, and the classifiers are then combined through a voting process. Learn+ however, generates additional ensembles as new data become available, and uses a different distribution update rule to resample the data. While AdaBoost was originally designed to improve the performance of a weak classifier, whether it can still achieve incremental learning through its ensemble structure is still an open question. In this paper, we compare the incremental learning ability of AdaBoost.M1 and Learn under very hostile nonstationary learning environments, which may introduce new concept classes. We also compare the algorithms under several combination rules to determine which of the three key components - ensemble structure, resampling procedure, or the combination rule - has the primary impact on incremental learning in nonstationary environments.

    Original languageEnglish (US)
    Title of host publication2006 IEEE International Conference on Systems, Man and Cybernetics
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages4838-4844
    Number of pages7
    ISBN (Print)1424401003, 9781424401000
    DOIs
    StatePublished - Jan 1 2006
    Event2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan, Province of China
    Duration: Oct 8 2006Oct 11 2006

    Publication series

    NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
    Volume6
    ISSN (Print)1062-922X

    Other

    Other2006 IEEE International Conference on Systems, Man and Cybernetics
    Country/TerritoryTaiwan, Province of China
    CityTaipei
    Period10/8/0610/11/06

    All Science Journal Classification (ASJC) codes

    • Engineering(all)

    Fingerprint

    Dive into the research topics of 'Comparison of ensemble techniques for incremental learning of new concept classes under hostile non-stationary environments'. Together they form a unique fingerprint.

    Cite this