Abstract:
: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.
journal_name
Comput Intell Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Guo H,Wang J,Ao W,He Ydoi
10.1155/2018/4058403subject
Has Abstractpub_date
2018-06-26 00:00:00pages
4058403eissn
1687-5265issn
1687-5273journal_volume
2018pub_type
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