Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/316598 
Year of Publication: 
2021
Citation: 
[Journal:] East Asian Economic Review (EAER) [ISSN:] 2508-1667 [Volume:] 25 [Issue:] 4 [Year:] 2021 [Pages:] 403-424
Publisher: 
Korea Institute for International Economic Policy (KIEP), Sejong-si
Abstract: 
AdaBoost tweaks the sample weight for each training set used in the iterative process, however, it is demonstrated that it provides more correlated errors as the boosting iteration proceeds if models' accuracy is high enough. Therefore, in this study, we propose a novel way to improve the performance of the existing AdaBoost algorithm by employing heterogeneous models and a stochastic twist. By employing the heterogeneous ensemble, it ensures different models that have a different initial assumption about the data are used to improve on diversity. Also, by using a stochastic algorithm with a decaying convergence rate, the model is designed to balance out the trade-off between model prediction performance and model convergence. The result showed that the stochastic algorithm with decaying convergence rate's did have a improving effect and outperformed other existing boosting techniques.
Subjects: 
Machine Learning
AdaBoost
XGBoost
Decaying Convergence Rate
JEL: 
C32
C50
G12
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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