Classifier fusion using shared sampling distribution for boosting

Costin Barbu; Raja Iqbal; Peng Jing

doi:10.1109/ICDM.2005.40

Classifier fusion using shared sampling distribution for boosting

Costin Barbu, Raja Iqbal, Peng Jing

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

We present a new framework for classifier fusion that uses a shared sampling distribution for obtaining a weighted classifier ensemble. The weight update process is self regularizing as subsequent classifiers trained on the disjoint views rectify the bias introduced by any classifier in preceding iterations. We provide theoretical guarantees that our approach indeed provides results which are better than the case when boosting is performed separately on different views. The results are shown to outperform other classifier fusion strategies on a well known texture image database.

Original language	English
Title of host publication	Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	8-15
Number of pages	8
ISBN (Print)	0769522785, 9780769522784
DOIs	https://doi.org/10.1109/ICDM.2005.40
State	Published - 2005
Event	5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States Duration: 27 Nov 2005 → 30 Nov 2005

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Other

Other	5th IEEE International Conference on Data Mining, ICDM 2005
Country/Territory	United States
City	Houston, TX
Period	27/11/05 → 30/11/05

Access to Document

10.1109/ICDM.2005.40

Cite this

Barbu, C., Iqbal, R., & Jing, P. (2005). Classifier fusion using shared sampling distribution for boosting. In Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005 (pp. 8-15). Article 1565659 (Proceedings - IEEE International Conference on Data Mining, ICDM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2005.40

@inproceedings{7fd1c67012c04b389ac3cd36f8afacc1,

title = "Classifier fusion using shared sampling distribution for boosting",

abstract = "We present a new framework for classifier fusion that uses a shared sampling distribution for obtaining a weighted classifier ensemble. The weight update process is self regularizing as subsequent classifiers trained on the disjoint views rectify the bias introduced by any classifier in preceding iterations. We provide theoretical guarantees that our approach indeed provides results which are better than the case when boosting is performed separately on different views. The results are shown to outperform other classifier fusion strategies on a well known texture image database.",

author = "Costin Barbu and Raja Iqbal and Peng Jing",

year = "2005",

doi = "10.1109/ICDM.2005.40",

language = "English",

isbn = "0769522785",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "8--15",

booktitle = "Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005",

note = "5th IEEE International Conference on Data Mining, ICDM 2005 ; Conference date: 27-11-2005 Through 30-11-2005",

}

Barbu, C, Iqbal, R & Jing, P 2005, Classifier fusion using shared sampling distribution for boosting. in Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005., 1565659, Proceedings - IEEE International Conference on Data Mining, ICDM, Institute of Electrical and Electronics Engineers Inc., pp. 8-15, 5th IEEE International Conference on Data Mining, ICDM 2005, Houston, TX, United States, 27/11/05. https://doi.org/10.1109/ICDM.2005.40

Classifier fusion using shared sampling distribution for boosting. / Barbu, Costin; Iqbal, Raja; Jing, Peng.
Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005. Institute of Electrical and Electronics Engineers Inc., 2005. p. 8-15 1565659 (Proceedings - IEEE International Conference on Data Mining, ICDM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We present a new framework for classifier fusion that uses a shared sampling distribution for obtaining a weighted classifier ensemble. The weight update process is self regularizing as subsequent classifiers trained on the disjoint views rectify the bias introduced by any classifier in preceding iterations. We provide theoretical guarantees that our approach indeed provides results which are better than the case when boosting is performed separately on different views. The results are shown to outperform other classifier fusion strategies on a well known texture image database.

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Classifier fusion using shared sampling distribution for boosting

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