New ensemble machine learning method for classification and prediction on gene expression data.
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.
Wang
Ching Wei
CW
Dept. of Comput. & Informatics, Univ. of Lincoln, UK. cweiwang@lincoln.ac.uk
D000465:Algorithms; D001185:Artificial Intelligence; D001698:Biomedical Engineering; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000235:genetics