Title

Ensemble of fast learning stochastic gradient boosting

Document Type

Article

Publication Date

1-1-2022

Abstract

Boosting is one of the most popular and powerful learning algorithms. However, due to its sequential nature in model fitting, the computational time of boosting algorithm can be prohibitive for big data analysis. In this paper, we proposed a parallel framework for boosting algorithm, called Ensemble of Fast Learning Stochastic Gradient Boosting (EFLSGB). The proposed EFLSGB is well suited for parallel execution, and therefore, can substantially reduce the computational time. Analysis of simulated and real datasets demonstrates that EFLSGB achieves highly competitive prediction accuracy in comparison with gradient tree boosting.

Publication Source (Journal or Book title)

Communications in Statistics: Simulation and Computation

First Page

40

Last Page

52

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