"Generalized bagging" by Junsik Kim, Ji Meng Loh et al.
 

Generalized bagging

Document Type

Article

Publication Date

12-1-2021

Abstract

This paper presents a generalization of the bagging procedure by using smoothed bootstrap with bagging, a procedure we call generalized bagging. Our generalized bagging method unifies input and output smearing, in the sense that noise is added to both the input and the output, so that input smearing and output smearing become special cases of generalized bagging. We discuss the choice of optimal smoothing parameter to control the variance of the added noise in the smoothed bootstrap. Our simulation studies show that the proposed method outperforms other competing methods, when the variance of the error term is large. We also demonstrate the performance of the proposed procedure with real datasets.

Identifier

85103209717 (Scopus)

Publication Title

Journal of the Korean Statistical Society

External Full Text Location

https://doi.org/10.1007/s42952-021-00114-8

ISSN

12263192

First Page

1219

Last Page

1237

Issue

4

Volume

50

Grant

HI19C0378

Fund Ref

Ministry of Health and Welfare

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