Constructive hidden nodes selection of extreme learning machine for regression

Authors:
Yuan Lan;Yeng Chai Soh;Guang-Bin Huang
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore
Venue:
Neurocomputing
Year:
2010

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Abstract

In this paper, we attempt to address the architectural design of ELM regressor by applying a constructive method on the basis of ELM algorithm. After the nonlinearities of ELM network are fixed by randomly generating the parameters, the network will correspond to a linear regression model. The selection of hidden nodes can then be regarded as a subset model selection in linear regression. The proposed constructive hidden nodes selection for ELM (referred to as CS-ELM) selects the optimal number of hidden nodes when the unbiased risk estimation based criterion C"P reaches the minimum value. A comparison of the proposed CS-ELM with other model selection algorithms of ELM is evaluated on several real benchmark regression applications. And the empirical study shows that CS-ELM leads to a compact network structure automatically.