swMATH ID: 
23498

Software Authors: 
Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.

Description: 
Bayesian additive regression trees using Bayesian model averaging. Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However, for datasets where the number of variables p is large the algorithm can become inefficient and computationally expensive. Another method which is popular for highdimensional data is random forests, a machine learning algorithm which grows trees using a greedy search for the best split points. However, its default implementation does not produce probabilistic estimates or predictions. We propose an alternative fitting algorithm for BART called BARTBMA, which uses Bayesian model averaging and a greedy search algorithm to obtain a posterior distribution more efficiently than BART for datasets with large p. BARTBMA incorporates elements of both BART and random forests to offer a modelbased algorithm which can deal with highdimensional data. We have found that BARTBMA can be run in a reasonable time on a standard laptop for the “small n large p” scenario which is common in many areas of bioinformatics. We showcase this method using simulated data and data from two real proteomic experiments, one to distinguish between patients with cardiovascular disease and controls and another to classify aggressive from nonaggressive prostate cancer. We compare our results to their main competitors. Open source code written in R and Rcpp to run BARTBMA can be found at: https://github.com/BelindaHernandez/BARTBMA.git. 
Homepage: 
https://github.com/BelindaHernandez/BARTBMA

Source Code: 
https://github.com/BelindaHernandez/BARTBMA

Dependencies: 
R 
Keywords: 
Bayesian additive regression trees;
Bayesian model averaging;
random forest;
biomarker selection;
small \(n\) large \(p\)

Related Software: 
BartPy;
R;
bartMachine;
randomForest;
tsbart;
GitHub;
AdaBoost.MH;
ElemStatLearn;
possum;
tsBCF;
grf;
inTrees;
conformal;
dbarts;
huge;
Rcpp;
GeneSrF;
BayesTree;
changepoint

Cited in: 
6 Publications
