Biclustering-Driven Ensemble of Bayesian Belief Network Classifiers for Underdetermined Problems
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1439-1444.
Abstract
In this paper, we present BENCH (BiclusteringdrivenENsemble of Classifiers), an algorithm toconstruct an ensemble of classifiers through concurrentfeature and data point selection guided byunsupervised knowledge obtained from biclustering.BENCH is designed for underdeterminedproblems. In our experiments, we use Bayesian BeliefNetwork (BBN) classifiers as base classifiers inthe ensemble; however, BENCH can be applied toother classification models as well. We show thatBENCH is able to increase prediction accuracy ofa single classifier and traditional ensemble of classifiersby up to 15% on three microarray datasetsusing various weighting schemes for combining individualpredictions in the ensemble.
@inproceedings{ijcai2011p243,
title = {Biclustering-Driven Ensemble of Bayesian Belief Network Classifiers for Underdetermined Problems},
author = {Pansombut, Tatdow and Hendrix, William and Gao, Zekai Jacob and Harrison, Brent E. and Samatova, Nagiza F.},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1439--1444},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-243},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-243},
}
TY - CPAPER TI - Biclustering-Driven Ensemble of Bayesian Belief Network Classifiers for Underdetermined Problems AU - Pansombut, Tatdow AU - Hendrix, William AU - Gao, Zekai Jacob AU - Harrison, Brent E. AU - Samatova, Nagiza F. T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1439 EP - 1444 DO - 10.5591/978-1-57735-516-8/IJCAI11-243 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-243 ER -Copied
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