12,000

We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK

93%

93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > Evolving extended naive Bayes classifiers
View graph of relations

« Back

Evolving extended naive Bayes classifiers

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date12/2006
Host publicationData Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
PublisherIEEE
Pages643-647
Number of pages5
ISBN (Print)0-7695-2702-7
Original languageEnglish

Conference

ConferenceSixth IEEE International Conference on Data Mining.
CityHong Kong
Period1/12/06 → …

Conference

ConferenceSixth IEEE International Conference on Data Mining.
CityHong Kong
Period1/12/06 → …

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

Bibliographic note

"©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."