The Adaptive Ensemble of OP-ELM Using Forward-Backward Selection

Bo Han; Bo He; Meng Meng Ma

doi:10.4028/www.scientific.net/AMM.427-429.1666

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 427-429The Adaptive Ensemble of OP-ELM Using...

The Adaptive Ensemble of OP-ELM Using Forward-Backward Selection

Abstract:

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance in regression or classification applications, but it has a weak robustness. In this paper, a new approach called The Adaptive Ensemble Of OP-ELM using Forward-Backward Selection (AEOP-ELM) is presented, it consists of two significant steps, firstly, we use forward-Backward selection algorithm to select the inputs which will ensure the robustness of the output, then, we train several independent OP-ELM models, and we test them iteratively to find the adaptive weights which will improve the accuracy of the output. The experiments indicate the AEOP-ELM achieves a better robustness than the original ELM as well as a better accuracy.

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Periodical:

Applied Mechanics and Materials (Volumes 427-429)

Pages:

1666-1669

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.427-429.1666

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Online since:

September 2013

Authors:

Bo Han, Bo He, Meng Meng Ma

Keywords:

Accuracy, Adaptive Ensemble, Forward-Backward Selection, Op-ELM, Robustness

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