Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data

Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. The wide acceptance can be attributed to its robustness to high dimensionality problem. However, when the high-dimensional data is a sparse one, RF procedures are ineffi...

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Main Author:	Oyebayo, Olaniran Ridwan
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	QA76.75-76.765 Computer software
Online Access:	http://eprints.uthm.edu.my/326/1/24p%20OLANIRAN%20RIDWAN%20OYEBAYO.pdf http://eprints.uthm.edu.my/326/2/OLANIRAN%20RIDWAN%20OYEBAYO%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/326/3/OLANIRAN%20RIDWAN%20OYEBAYO%20WATERMARK.pdf
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spelling	my-uthm-ep.3262021-07-21T04:51:58Z Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data 2018-11 Oyebayo, Olaniran Ridwan QA76.75-76.765 Computer software Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. The wide acceptance can be attributed to its robustness to high dimensionality problem. However, when the high-dimensional data is a sparse one, RF procedures are inefficient. Thus, this thesis aims at improving the efficiency of RF by providing a probabilistic framework using Bayesian reasoning. The modification comprises of two main modelling problems: high-dimensionality and missing data. These problems were extensively studied within the scope of classification (binary and multi-class) and regression (linear and survival). The new procedure called Bayesian Random Forest (BRF) focuses on modification of terminal node parameter estimation and selection of random subsets for splitting. BRF algorithm combines the strengths of random subset and greedy selection procedures in creating new maximal ordered variable relevance weights. These weights are in turn used to develop new impurity functions for selecting optimal splits for each tree in a forest. BRF works mainly because the maximal weights are computed using a data-driven procedure called bootstrap prior which was shown to satisfy the uniformly minimum variance property under mild regularity conditions. In addition, BRF ensures that important variables are selected at each subset selection step, thus reducing false signals and eventually improving accuracy of models. As a further extension, missing covariates problem was also handled by pre-imputing the variables using Multivariate Imputation by Chain Equation (MICE) before building forests. Performance analysis was achieved using simulated and eighteen real-life classification and regression microarray cancer datasets. Empirical results from the data analysis established appreciable supremacy over RF and several other competing methods. Keyword: Random Forest, Bayesian Inference, Classification, Regression, Missing Data. 2018-11 Thesis http://eprints.uthm.edu.my/326/ http://eprints.uthm.edu.my/326/1/24p%20OLANIRAN%20RIDWAN%20OYEBAYO.pdf text en public http://eprints.uthm.edu.my/326/2/OLANIRAN%20RIDWAN%20OYEBAYO%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/326/3/OLANIRAN%20RIDWAN%20OYEBAYO%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Faculty of Applied Sciences and Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA76.75-76.765 Computer software
spellingShingle	QA76.75-76.765 Computer software Oyebayo, Olaniran Ridwan Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
description	Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. The wide acceptance can be attributed to its robustness to high dimensionality problem. However, when the high-dimensional data is a sparse one, RF procedures are inefficient. Thus, this thesis aims at improving the efficiency of RF by providing a probabilistic framework using Bayesian reasoning. The modification comprises of two main modelling problems: high-dimensionality and missing data. These problems were extensively studied within the scope of classification (binary and multi-class) and regression (linear and survival). The new procedure called Bayesian Random Forest (BRF) focuses on modification of terminal node parameter estimation and selection of random subsets for splitting. BRF algorithm combines the strengths of random subset and greedy selection procedures in creating new maximal ordered variable relevance weights. These weights are in turn used to develop new impurity functions for selecting optimal splits for each tree in a forest. BRF works mainly because the maximal weights are computed using a data-driven procedure called bootstrap prior which was shown to satisfy the uniformly minimum variance property under mild regularity conditions. In addition, BRF ensures that important variables are selected at each subset selection step, thus reducing false signals and eventually improving accuracy of models. As a further extension, missing covariates problem was also handled by pre-imputing the variables using Multivariate Imputation by Chain Equation (MICE) before building forests. Performance analysis was achieved using simulated and eighteen real-life classification and regression microarray cancer datasets. Empirical results from the data analysis established appreciable supremacy over RF and several other competing methods. Keyword: Random Forest, Bayesian Inference, Classification, Regression, Missing Data.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Oyebayo, Olaniran Ridwan
author_facet	Oyebayo, Olaniran Ridwan
author_sort	Oyebayo, Olaniran Ridwan
title	Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
title_short	Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
title_full	Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
title_fullStr	Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
title_full_unstemmed	Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
title_sort	bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Applied Sciences and Technology
publishDate	2018
url	http://eprints.uthm.edu.my/326/1/24p%20OLANIRAN%20RIDWAN%20OYEBAYO.pdf http://eprints.uthm.edu.my/326/2/OLANIRAN%20RIDWAN%20OYEBAYO%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/326/3/OLANIRAN%20RIDWAN%20OYEBAYO%20WATERMARK.pdf
_version_	1747830583043555328

Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data

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