Wang, Ching-Wei (2006) New ensemble machine learning method for classification and prediction on gene expression data. In: Proceedings of the 28th IEEE EMBS Annual International Conference. Institute of Electrical and Electronics Engineers, Inc, New York, USA, pp. 3478-3481. ISBN 1424400333

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Abstract

–A reliable and precise classification of tumours is essential for successful treatment of cancer. Recent researches have confirmed the utility of ensemble machine learning algorithms for gene expression data analysis. In this paper, a new ensemble machine learning algorithm is proposed for classification and prediction on gene expression data. The algorithm is tested and compared with three popular adopted ensembles, i.e. bagging, boosting and arcing. The results show that the proposed algorithm greatly outperforms existing methods, achieving high accuracy over 12 gene expression datasets.

Item Type:	Book Section
Additional Information:	–A reliable and precise classification of tumours is essential for successful treatment of cancer. Recent researches have confirmed the utility of ensemble machine learning algorithms for gene expression data analysis. In this paper, a new ensemble machine learning algorithm is proposed for classification and prediction on gene expression data. The algorithm is tested and compared with three popular adopted ensembles, i.e. bagging, boosting and arcing. The results show that the proposed algorithm greatly outperforms existing methods, achieving high accuracy over 12 gene expression datasets.
Keywords:	ensemble machine learning, pattern recognition, microarray
Subjects:	G Mathematical and Computer Sciences > G400 Computer Science
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Bev Jones
Date Deposited:	27 Nov 2006
Last Modified:	13 Mar 2013 08:22
URI:	http://eprints.lincoln.ac.uk/id/eprint/115

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