LARSEN-ELM: Selective ensemble of extreme learning machines using LARS for blended data

Han, B., He, B., Nian, R., Ma, M., Zhang, S., Li, M. and Lendasse, A. (2015) LARSEN-ELM: Selective ensemble of extreme learning machines using LARS for blended data. Neurocomputing, 149(Pt. A), pp. 285-294. (doi: 10.1016/j.neucom.2014.01.069)

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Abstract

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called “LARSEN-ELM” for overcoming this problem. In our paper, we would like to show two key steps in LARSEN-ELM. In the first step, preprocessing, we select the input variables highly related to the output using least angle regression (LARS). In the second step, training, we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In the experiments, we apply a sum of two sines and four datasets from UCI repository to verify the robustness of our approach. The experimental results show that compared with original ELM and other methods such as OP-ELM, GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance while keeping a relatively high speed.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Li, Dr David
Authors: Han, B., He, B., Nian, R., Ma, M., Zhang, S., Li, M., and Lendasse, A.
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
College/School:College of Science and Engineering > School of Engineering
Journal Name:Neurocomputing
Publisher:Elsevier
ISSN:0925-2312
ISSN (Online):1872-8286
Published Online:16 September 2014
Copyright Holders:Copyright © 2015 Elsevier B.V.
First Published:First published in Neurocomputing 149(Pt. A):285-294
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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