Bioinformatics Advance Access originally published online on October 5, 2004
Bioinformatics 2004 20(18):3583-3593; doi:10.1093/bioinformatics/bth447
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Bioinformatics vol. 20 issue 18 © Oxford University Press 2004; all rights reserved.
BagBoosting for tumor classification with gene expression data
Seminar für Statistik, ETH Zürich, CH-8092 Switzerland
Received on June 5, 2004; revised on July 5, 2004; accepted on July 9, 2004
Advance Access Publication October 5, 2004
Motivation: Microarray experiments are expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. They create a need for class prediction tools, which can deal with a large number of highly correlated input variables, perform feature selection and provide class probability estimates that serve as a quantification of the predictive uncertainty. A very promising solution is to combine the two ensemble schemes bagging and boosting to a novel algorithm called BagBoosting.
Results: When bagging is used as a module in boosting, the resulting classifier consistently improves the predictive performance and the probability estimates of both bagging and boosting on real and simulated gene expression data. This quasi-guaranteed improvement can be obtained by simply making a bigger computing effort. The advantageous predictive potential is also confirmed by comparing BagBoosting to several established class prediction tools for microarray data.
Availability: Software for the modified boosting algorithms, for benchmark studies and for the simulation of microarray data are available as an R package under GNU public license at http://stat.ethz.ch/~dettling/bagboost.html
Contact: dettling{at}stat.math.ethz.ch
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