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Evolving extended naive Bayes classifiers

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Evolving extended naive Bayes classifiers. / Klawonn, Frank; Angelov, Plamen.
Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on. IEEE, 2006. p. 643-647.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Klawonn, F & Angelov, P 2006, Evolving extended naive Bayes classifiers. in Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on. IEEE, pp. 643-647, Sixth IEEE International Conference on Data Mining., Hong Kong, 1/12/06. https://doi.org/10.1109/ICDMW.2006.74

APA

Klawonn, F., & Angelov, P. (2006). Evolving extended naive Bayes classifiers. In Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on (pp. 643-647). IEEE. https://doi.org/10.1109/ICDMW.2006.74

Vancouver

Klawonn F, Angelov P. Evolving extended naive Bayes classifiers. In Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on. IEEE. 2006. p. 643-647 doi: 10.1109/ICDMW.2006.74

Author

Klawonn, Frank ; Angelov, Plamen. / Evolving extended naive Bayes classifiers. Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on. IEEE, 2006. pp. 643-647

Bibtex

@inproceedings{83774a66420c423d8dafdffebd9aaf81,
title = "Evolving extended naive Bayes classifiers",
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",
author = "Frank Klawonn and Plamen Angelov",
note = "{"}{\textcopyright}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.{"}; Sixth IEEE International Conference on Data Mining. ; Conference date: 01-12-2006",
year = "2006",
month = dec,
doi = "10.1109/ICDMW.2006.74",
language = "English",
isbn = "0-7695-2702-7",
pages = "643--647",
booktitle = "Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Evolving extended naive Bayes classifiers

AU - Klawonn, Frank

AU - Angelov, Plamen

N1 - "©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."

PY - 2006/12

Y1 - 2006/12

N2 - 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

AB - 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

U2 - 10.1109/ICDMW.2006.74

DO - 10.1109/ICDMW.2006.74

M3 - Conference contribution/Paper

SN - 0-7695-2702-7

SP - 643

EP - 647

BT - Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on

PB - IEEE

T2 - Sixth IEEE International Conference on Data Mining.

Y2 - 1 December 2006

ER -