An evaluation of using ensembles of classifiers for predictions based on genomic and proteomic data
Classification of expression profiles to predict disease characteristics of for example cancer is a common application in high-throughput gene and protein expression research. Cross-validation is often used to optimize design of classifiers, with the aim to construct an optimal single classifier. In this work, we explore if classification performance can be improved by aggregating classifiers into
