How to use boosting for tumor classification with gene expression data

Motivation: Microarray experiments generate large datasets with expression values for thousands of genes but not more than a few dozens of samples. Accurate supervised classification of tissue samples in such high-dimensional problems is difficult but often crucial for successful diagnosis and treatment. A promising way to meet this challenge is by using boosting in conjunction with decision trees. Results: We demonstrate that the generic boosting al-gorithm needs some modification to become an accurate classifier in the context of gene expression data. In par-ticular, we present a feature preselection method, a more robust boosting procedure and a new approach for multi-categorical problems. This allows for slight to drastic in-crease in performance and yields competitive results on several publicly available datasets. Availability: Software for the modified boosting algorithms as well as for decision trees is available for free in R at