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 |
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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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