SGB-ELM: An Advanced Stochastic Gradient Boosting-Based Ensemble Scheme for Extreme Learning Machine

doi:10.1155/2018/4058403

	SGB-ELM: An Advanced Stochastic Gradient Boosting-Based Ensemble Scheme for Extreme Learning Machine
	Guo, Hua; Wang, Jikui; Ao, Wei ; He, Yulin
	2018
发表期刊	COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
卷号	2018
摘要	A novel ensemble scheme for extreme learning machine (ELM), named Stochastic Gradient Boosting-based Extreme Learning Machine (SGB-ELM), is proposed in this paper. Instead of incorporating the stochastic gradient boosting method into ELM ensemble procedure primitively, SGB-ELM constructs a sequence of weak ELMs where each individual ELM is trained additively by optimizing the regularized objective. Specifically, we design an objective function based on the boosting mechanism where a regularization item is introduced simultaneously to alleviate overfitting. Then the derivation formula aimed at solving the output-layer weights of each weak ELM is determined using the second-order optimization. As the derivation formula is hard to be analytically calculated and the regularized objective tends to employ simple functions, we take the output-layer weights learned by the current pseudo residuals as an initial heuristic item and thus obtain the optimal output-layer weights by using the derivation formula to update the heuristic item iteratively. In comparison with several typical ELM ensemble methods, SGB-ELM achieves better generalization performance and predicted robustness, which demonstrates the feasibility and effectiveness of SGB-ELM.
关键词	Iterative methods Knowledge acquisition Machine learning Optimization Boosting mechanism Derivation formulas Ensemble methods Extreme learning machine Generalization performance Objective functions Second order optimization Stochastic gradient boosting
DOI	10.1155/2018/4058403
收录类别	SCI ; SCIE ; EI
ISSN	1687-5265
语种	英语
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目	Mathematical & Computational Biology ; Neurosciences
WOS记录号	WOS:000437974100001
出版者	HINDAWI LTD
EI入藏号	20182905551679
原始文献类型	Article
EISSN	1687-5273
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/11588
专题	信息工程与人工智能学院
作者单位	1.Lanzhou Univ Finance & Econ, Sch Informat Engn, Lanzhou 730020, Gansu, Peoples R China; 2.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
推荐引用方式 GB/T 7714	Guo, Hua,Wang, Jikui,Ao, Wei,et al. SGB-ELM: An Advanced Stochastic Gradient Boosting-Based Ensemble Scheme for Extreme Learning Machine[J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2018,2018.
APA	Guo, Hua,Wang, Jikui,Ao, Wei,&He, Yulin.(2018).SGB-ELM: An Advanced Stochastic Gradient Boosting-Based Ensemble Scheme for Extreme Learning Machine.COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2018.
MLA	Guo, Hua,et al."SGB-ELM: An Advanced Stochastic Gradient Boosting-Based Ensemble Scheme for Extreme Learning Machine".COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018(2018).

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