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Evolving extended naive Bayes classifiers

Klawonn, Frank and Angelov, Plamen (2006) Evolving extended naive Bayes classifiers. In: Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on. IEEE, pp. 643-647. ISBN 0-7695-2702-7

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    Abstract

    Naive Bayes classifiers are a very simple, but often effective tool for classification problems, although they are based on independence assumptions that do not hold in most cases. Extended naive Bayes classifiers also rely on independence assumptions, but break them down to artificial subclasses, in this way becoming more powerful than ordinary naive Bayes classifiers. Since the involved computations for Bayes classifiers are basically generalised mean value calculations, they easily render themselves to incremental and online learning. However, for the extended naive Bayes classifiers it is necessary, to choose and construct the subclasses, a problem whose answer is not obvious, especially in the case of online learning. In this paper we propose an evolving extended naive Bayes classifier that can learn and evolve in an online manner (c) IEEE Press

    Item Type: Contribution in Book/Report/Proceedings
    Additional Information: "©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 935
    Deposited By: Dr. Plamen Angelov
    Deposited On: 11 Jan 2008 11:52
    Refereed?: No
    Published?: Published
    Last Modified: 10 Apr 2014 01:16
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/935

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