An ensemble of classifiers for coping with recurring contexts in data streams

Ioannis Katakis, Grigorios Tsoumakas, Ioannis Vlahavas

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

33 Citations (Scopus)

Abstract

This paper proposes a general framework for classifying data streams by exploiting incremental clustering in order to dynamically build and update an ensemble of incremental classifiers. To achieve this, a transformation function that maps batches of examples into a new conceptual feature space is proposed. The clustering algorithm is then applied in order to group different concepts and identify recurring contexts. The ensemble is produced by maintaining an classifier for every concept discovered in the stream2.

Original languageEnglish
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Pages763-764
Number of pages2
ISBN (Print)978158603891
DOIs
Publication statusPublished - 1 Jun 2008

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume178
ISSN (Print)0922-6389

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  • Cite this

    Katakis, I., Tsoumakas, G., & Vlahavas, I. (2008). An ensemble of classifiers for coping with recurring contexts in data streams. In Frontiers in Artificial Intelligence and Applications (pp. 763-764). (Frontiers in Artificial Intelligence and Applications; Vol. 178). IOS Press. https://doi.org/10.3233/978-1-58603-891-5-763