swMATH ID: 35655
Software Authors: Marcel Dettling
Description: R package boost: BagBoosting for tumor classification with gene expression data. 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
Homepage: https://academic.oup.com/bioinformatics/article/20/18/3583/202535
Dependencies: R
Related Software: ElemStatLearn; R; glmnet; rda; glasso; UCI-ml; penalizedLDA; mclust; impute; L1-MAGIC; AdaBoost.MH; sparcl; PDCO; GeneSrF; mboost; Bioconductor; Scikit; clusteval; MBCbook; AS 136
Cited in: 41 Publications

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