Improving Gene Expression Sample Classification Using Support Vector Machine Ensembles Aggregated by Boosting

Cancer Genomics - Proteomics

ANASTASIOS BEZERIANOS¹  ANDREI DRAGOMIR¹ 

[1] Department of Medical Physics, Medical School, University of Patras, Rio 26500, Greece Department of Medical Physics, Medical School, University of Patras, Rio 26500, Greece Department of Medical Physics, Medical School, University of Patras, Rio 26500, Greece

关键词: Gene expression;    classification;    support vector machines;    boosting;    ensemble learning;    feature selection;   

DOI  :  

来源: Delinasios GJ CO

【 摘 要 】

The molecular characterization of different tumor types using gene expression profiling is expected to uncover fundamental aspects related to cancer diagnosis and drug discovery. There is, therefore, a need for reliable, accurate sample classification tools, as well as methods for efficient identification of genes informative for the class discrimination. We propose a method based on Support Vector Machine (SVM) ensembles, trained within a boosting framework. The approach allows sequential training of classifiers on different data subsets, their aggregate yielding results superior to single SVM. Results from binary and multiclass classification experiments performed on several data sets are presented.

【 授权许可】

Unknown