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Correlated variable importance for random forests

Authors
Shin, Seung BeomCho, Hyung Jun
Issue Date
4월-2021
Publisher
KOREAN STATISTICAL SOC
Keywords
correlation; random forests; variable importance
Citation
KOREAN JOURNAL OF APPLIED STATISTICS, v.34, no.2, pp.177 - 190
Indexed
KCI
Journal Title
KOREAN JOURNAL OF APPLIED STATISTICS
Volume
34
Number
2
Start Page
177
End Page
190
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/137712
DOI
10.5351/KJAS.2021.34.2.177
ISSN
1225-066X
Abstract
Random forests is a popular method that improves the instability and accuracy of decision trees by ensembles. In contrast to increasing the accuracy, the ease of interpretation is sacrificed; hence, to compensate for this, variable importance is provided. The variable importance indicates which variable plays a role more importantly in constructing the random forests. However, when a predictor is correlated with other predictors, the variable importance of the existing importance algorithm may be distorted. The downward bias of correlated predictors may reduce the importance of truly important predictors. We propose a new algorithm remedying the downward bias of correlated predictors. The performance of the proposed algorithm is demonstrated by the simulated data and illustrated by the real data.
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정경대학 (통계학과)
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