Hernandez, Belinda and Raftery, Adrianm, and Pennington, Stephen R and Parnell, Andrew (2017) Bayesian Additive Regression Trees using Bayesian model averaging. Working Paper. arXiv.

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Abstract

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 high-dimensional 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 BART-BMA, which uses Bayesian model averaging and a greedy search algorithm to obtain a posterior distribution more efficiently than BART for datasets with large p. BART-BMA incorporates elements of both BART and random forests to offer a model-based algorithm which can deal with high-dimensional data. We have found that BART-BMA 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 non-aggressive prostate cancer. We compare our results to their main competitors. Open source code written in R and Rcpp to run BART-BMA can be found at: https://github.com/BelindaHernandez/BART-BMA.git.

Item Type:	Monograph (Working Paper)
Additional Information:	This is the preprint version of the published article, which is availabel at: Hernández, B., Raftery, A.E., Pennington, S.R. et al. Stat Comput (2018) 28: 869. https://doi.org/10.1007/s11222-017-9767-1
Keywords:	Bayesian Additive Regression Trees; Bayesian model averaging; Random forest; Biomarker selection; Small n large p;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	10262
Identification Number:	arXiv:1507.00181 [stat.CO]
Depositing User:	Andrew Parnell
Date Deposited:	03 Dec 2018 15:21
Publisher:	arXiv
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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