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
Recommended Citation
Kim, Junsik; Loh, Ji Meng; and Jang, Woncheol, "Generalized bagging" (2021). Faculty Publications. 3596.
https://digitalcommons.njit.edu/fac_pubs/3596