BagBoosting for tumor classification with gene expression data (2004)
| Venue: | Bioinformatics |
| Citations: | 79 - 1 self |
BibTeX
@MISC{Dettling04bagboostingfor,
author = {Marcel Dettling},
title = {BagBoosting for tumor classification with gene expression data },
year = {2004}
}
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Abstract
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.
