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

被引：0
|
作者：
Wang, Ching Wei [1 ]
机构：
[1] Univ Lincoln, Vis & Artificial Intelligence Grp, Dept Comp & Informat, Lincoln LN6 7TS, England
来源：
2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15 | 2006年
关键词：
ensemble machine learning; pattern recognition; microarray;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
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.
引用
收藏
页码：60 / 63
页数：4
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