Biclustering-driven ensemble of bayesian belief network classifiers for underdetermined problems

Tatdow Pansombut; William Hendrix; Zekai J. Gao; Brent E. Harrison; Nagiza F. Samatova

doi:10.5591/978-1-57735-516-8/IJCAI11-243

Biclustering-driven ensemble of bayesian belief network classifiers for underdetermined problems

Tatdow Pansombut, William Hendrix, Zekai J. Gao, Brent E. Harrison, Nagiza F. Samatova

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

6 Scopus citations

Abstract

In this paper, we present BENCH (Biclusteringdriven ENsemble of Classifiers), an algorithm to construct an ensemble of classifiers through concurrent feature and data point selection guided by unsupervised knowledge obtained from biclustering. BENCH is designed for underdetermined problems. In our experiments, we use Bayesian Belief Network (BBN) classifiers as base classifiers in the ensemble; however, BENCH can be applied to other classification models as well. We show that BENCH is able to increase prediction accuracy of a single classifier and traditional ensemble of classifiers by up to 15% on three microarray datasets using various weighting schemes for combining individual predictions in the ensemble.

Original language	English
Title of host publication	IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
Pages	1439-1445
Number of pages	7
DOIs	https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-243
State	Published - 2011
Event	22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia, Spain Duration: Jul 16 2011 → Jul 22 2011

Publication series

Name	IJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)	1045-0823

Conference

Conference	22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
Country/Territory	Spain
City	Barcelona, Catalonia
Period	7/16/11 → 7/22/11

ASJC Scopus subject areas

Artificial Intelligence

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10.5591/978-1-57735-516-8/IJCAI11-243

Cite this

Pansombut, T., Hendrix, W., Gao, Z. J., Harrison, B. E., & Samatova, N. F. (2011). Biclustering-driven ensemble of bayesian belief network classifiers for underdetermined problems. In IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence (pp. 1439-1445). (IJCAI International Joint Conference on Artificial Intelligence). https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-243

@inproceedings{c57df6c67a3b454fa268e983db8fd943,

title = "Biclustering-driven ensemble of bayesian belief network classifiers for underdetermined problems",

abstract = "In this paper, we present BENCH (Biclusteringdriven ENsemble of Classifiers), an algorithm to construct an ensemble of classifiers through concurrent feature and data point selection guided by unsupervised knowledge obtained from biclustering. BENCH is designed for underdetermined problems. In our experiments, we use Bayesian Belief Network (BBN) classifiers as base classifiers in the ensemble; however, BENCH can be applied to other classification models as well. We show that BENCH is able to increase prediction accuracy of a single classifier and traditional ensemble of classifiers by up to 15% on three microarray datasets using various weighting schemes for combining individual predictions in the ensemble.",

author = "Tatdow Pansombut and William Hendrix and Gao, {Zekai J.} and Harrison, {Brent E.} and Samatova, {Nagiza F.}",

year = "2011",

doi = "10.5591/978-1-57735-516-8/IJCAI11-243",

language = "English",

isbn = "9781577355120",

series = "IJCAI International Joint Conference on Artificial Intelligence",

pages = "1439--1445",

booktitle = "IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence",

note = "22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 ; Conference date: 16-07-2011 Through 22-07-2011",

}

Pansombut, T, Hendrix, W, Gao, ZJ, Harrison, BE & Samatova, NF 2011, Biclustering-driven ensemble of bayesian belief network classifiers for underdetermined problems. in IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence. IJCAI International Joint Conference on Artificial Intelligence, pp. 1439-1445, 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011, Barcelona, Catalonia, Spain, 7/16/11. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-243

Biclustering-driven ensemble of bayesian belief network classifiers for underdetermined problems. / Pansombut, Tatdow; Hendrix, William; Gao, Zekai J. et al.
IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence. 2011. p. 1439-1445 (IJCAI International Joint Conference on Artificial Intelligence).

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

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AB - In this paper, we present BENCH (Biclusteringdriven ENsemble of Classifiers), an algorithm to construct an ensemble of classifiers through concurrent feature and data point selection guided by unsupervised knowledge obtained from biclustering. BENCH is designed for underdetermined problems. In our experiments, we use Bayesian Belief Network (BBN) classifiers as base classifiers in the ensemble; however, BENCH can be applied to other classification models as well. We show that BENCH is able to increase prediction accuracy of a single classifier and traditional ensemble of classifiers by up to 15% on three microarray datasets using various weighting schemes for combining individual predictions in the ensemble.

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Biclustering-driven ensemble of bayesian belief network classifiers for underdetermined problems

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