Enhancement of Random Forests Using Trees with Oblique Splits

This work presents an enhancement to the classification tree algorithm which forms the basis for Random Forests. Differently from the classical tree-based methods that focus on one variable at a time to separate the observations, the new algorithm performs the search for the best split in two-dimens...

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Main Author:	Parfionovas, Andrejus
Format:	Others
Published:	DigitalCommons@USU 2013
Subjects:	Classification Machine Learning Statistics and Probability
Online Access:	http://digitalcommons.usu.edu/etd/1508 http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=2540&context=etd

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spelling	ndltd-UTAHS-oai-http---digitalcommons.usu.edu-do-oai--etd-25402013-05-23T04:25:35Z Enhancement of Random Forests Using Trees with Oblique Splits Parfionovas, Andrejus This work presents an enhancement to the classification tree algorithm which forms the basis for Random Forests. Differently from the classical tree-based methods that focus on one variable at a time to separate the observations, the new algorithm performs the search for the best split in two-dimensional space using a linear combination of variables. Besides the classification, the method can be used to determine variables interaction and perform feature extraction. Theoretical investigations and numerical simulations were used to analyze the properties and performance of the new approach. Comparison with other popular classification methods was performed using simulated and real data examples. The algorithm was implemented as an extension package for the statistical computing environment R and is available for free download under the GNU General Public License. 2013-05-01T07:00:00Z text application/pdf http://digitalcommons.usu.edu/etd/1508 http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=2540&context=etd Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact Andrew Wesolek (andrew.wesolek@usu.edu). All Graduate Theses and Dissertations DigitalCommons@USU Classification Machine Learning Statistics and Probability
collection	NDLTD
format	Others
sources	NDLTD
topic	Classification Machine Learning Statistics and Probability
spellingShingle	Classification Machine Learning Statistics and Probability Parfionovas, Andrejus Enhancement of Random Forests Using Trees with Oblique Splits
description	This work presents an enhancement to the classification tree algorithm which forms the basis for Random Forests. Differently from the classical tree-based methods that focus on one variable at a time to separate the observations, the new algorithm performs the search for the best split in two-dimensional space using a linear combination of variables. Besides the classification, the method can be used to determine variables interaction and perform feature extraction. Theoretical investigations and numerical simulations were used to analyze the properties and performance of the new approach. Comparison with other popular classification methods was performed using simulated and real data examples. The algorithm was implemented as an extension package for the statistical computing environment R and is available for free download under the GNU General Public License.
author	Parfionovas, Andrejus
author_facet	Parfionovas, Andrejus
author_sort	Parfionovas, Andrejus
title	Enhancement of Random Forests Using Trees with Oblique Splits
title_short	Enhancement of Random Forests Using Trees with Oblique Splits
title_full	Enhancement of Random Forests Using Trees with Oblique Splits
title_fullStr	Enhancement of Random Forests Using Trees with Oblique Splits
title_full_unstemmed	Enhancement of Random Forests Using Trees with Oblique Splits
title_sort	enhancement of random forests using trees with oblique splits
publisher	DigitalCommons@USU
publishDate	2013
url	http://digitalcommons.usu.edu/etd/1508 http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=2540&context=etd
work_keys_str_mv	AT parfionovasandrejus enhancementofrandomforestsusingtreeswithobliquesplits
_version_	1716585856166264832

Enhancement of Random Forests Using Trees with Oblique Splits

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