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New ensemble machine learning method for classification and prediction on gene expression data

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.

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 Science > School of Computer Science
ID Code:115
Deposited On:27 Nov 2006

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