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Bias corrections for Random Forest in regression using residual rotation

Title
Bias corrections for Random Forest in regression using residual rotation
Authors
SongJ.
Ewha Authors
송종우
SCOPUS Author ID
송종우scopus
Issue Date
2015
Journal Title
Journal of the Korean Statistical Society
ISSN
1226-3192JCR Link
Citation
vol. 44, no. 2, pp. 321 - 326
Keywords
Bias correctionRandom ForestRegression
Publisher
Korean Statistical Society
Indexed
SCIE; SCOPUS; KCI WOS scopus
Abstract
This paper studies bias correction methods for Random Forest in regression. Random Forest is a special bagging trees that can be used in regression and classification. It is a popular method because of its high prediction accuracy. However, we find that Random Forest can have significant bias in regression at times. We propose a method to reduce the bias of Random Forest in regression using residual rotation. The real data applications show that our method can reduce the bias of Random Forest significantly. © 2015 The Korean Statistical Society.
DOI
10.1016/j.jkss.2015.01.003
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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