Rotation of random forests for genomic and proteomic classification problems.

Random Forests have been recently widely used for different kinds of classification problems. One of them is classification of gene expression samples that is known as a problem with extremely high dimensionality, and therefore demands suited classification techniques. Due to its strong robustness with respect to large feature sets, Random Forests show significant increase of accuracy in comparison to other ensemble-based classifiers that were widely used before its introduction. In this chapter, we present another ensemble of decision trees called Rotation Forest and evaluate its classification performance on different microarray datasets. Rotation Forest can also be applied to different already existing ensembles of classifiers like Random Forest to improve their accuracy and robustness. This study presents evaluation of Rotation Forest classification technique based on decision trees as base classifiers and was evaluated on 14 different datasets with genomic and proteomic data. It is evident that Rotation Forest as well as the proposed rotation of Random Forests outperform most widely used ensembles of classifiers including Random Forests on majority of datasets.