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Enhancing extreme learning machines classification with moth-flame optimization technique

Extreme learning machine (ELM) algorithm assigns the input weights and biases in a "one-time stamp" fashion, this method makes the algorithm to be ill-conditioned and reduces its classification accuracy. The contribution of this work is the enhancement of the performance of ELM with the mo...

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Main Authors: Alade, Oyekale Abel, Sallehuddin, Roselina, Mohamed Radzi, Nor Haizan
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/98678/1/RoselinaSallehuddin2022_EnhancingExtremeLearningMachines.pdf
http://eprints.utm.my/id/eprint/98678/
http://dx.doi.org/10.11591/ijeecs.v26.i2.pp1027-1035
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spelling my.utm.986782023-01-30T04:52:15Z http://eprints.utm.my/id/eprint/98678/ Enhancing extreme learning machines classification with moth-flame optimization technique Alade, Oyekale Abel Sallehuddin, Roselina Mohamed Radzi, Nor Haizan QA75 Electronic computers. Computer science Extreme learning machine (ELM) algorithm assigns the input weights and biases in a "one-time stamp" fashion, this method makes the algorithm to be ill-conditioned and reduces its classification accuracy. The contribution of this work is the enhancement of the performance of ELM with the moth-flame optimization (MFO) algorithm to improve classification accuracy. A hybrid of the Moth-flame optimization and extreme learning machine (MFO-ELM) algorithm is implemented in MATLAB. MFO ensures a concurrent simulation of exploration and exploitation of the search space to select an optimum candidate solution. The candidate solution is reshaped into input weights and biases for ELM classification. The hybrid algorithm is validated on five life-selected datasets. The performance improvement of MFO-ELM is compared with ELM-optimized particle swarm optimization (PSO-ELM) and competitive swarm optimization (CSO-ELM) algorithms. The improvement rates are qualitatively and quantitatively evaluated to show the improvement of MFO-ELM on ELM and the other meta-heuristic algorithms. MFO-ELM improved the accuracies of the basic ELM in all 100% of the simulations and performed better than the other meta-heuristic algorithms in 80% of the simulations. The performance of MFO-ELM is more competitive, and it is recommended for solving classification problems. Institute of Advanced Engineering and Science 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/98678/1/RoselinaSallehuddin2022_EnhancingExtremeLearningMachines.pdf Alade, Oyekale Abel and Sallehuddin, Roselina and Mohamed Radzi, Nor Haizan (2022) Enhancing extreme learning machines classification with moth-flame optimization technique. Indonesian Journal of Electrical Engineering and Computer Science, 26 (2). pp. 1027-1035. ISSN 2502-4752 http://dx.doi.org/10.11591/ijeecs.v26.i2.pp1027-1035 DOI: 10.11591/ijeecs.v26.i2.pp1027-1035
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Alade, Oyekale Abel
Sallehuddin, Roselina
Mohamed Radzi, Nor Haizan
Enhancing extreme learning machines classification with moth-flame optimization technique
description Extreme learning machine (ELM) algorithm assigns the input weights and biases in a "one-time stamp" fashion, this method makes the algorithm to be ill-conditioned and reduces its classification accuracy. The contribution of this work is the enhancement of the performance of ELM with the moth-flame optimization (MFO) algorithm to improve classification accuracy. A hybrid of the Moth-flame optimization and extreme learning machine (MFO-ELM) algorithm is implemented in MATLAB. MFO ensures a concurrent simulation of exploration and exploitation of the search space to select an optimum candidate solution. The candidate solution is reshaped into input weights and biases for ELM classification. The hybrid algorithm is validated on five life-selected datasets. The performance improvement of MFO-ELM is compared with ELM-optimized particle swarm optimization (PSO-ELM) and competitive swarm optimization (CSO-ELM) algorithms. The improvement rates are qualitatively and quantitatively evaluated to show the improvement of MFO-ELM on ELM and the other meta-heuristic algorithms. MFO-ELM improved the accuracies of the basic ELM in all 100% of the simulations and performed better than the other meta-heuristic algorithms in 80% of the simulations. The performance of MFO-ELM is more competitive, and it is recommended for solving classification problems.
format Article
author Alade, Oyekale Abel
Sallehuddin, Roselina
Mohamed Radzi, Nor Haizan
author_facet Alade, Oyekale Abel
Sallehuddin, Roselina
Mohamed Radzi, Nor Haizan
author_sort Alade, Oyekale Abel
title Enhancing extreme learning machines classification with moth-flame optimization technique
title_short Enhancing extreme learning machines classification with moth-flame optimization technique
title_full Enhancing extreme learning machines classification with moth-flame optimization technique
title_fullStr Enhancing extreme learning machines classification with moth-flame optimization technique
title_full_unstemmed Enhancing extreme learning machines classification with moth-flame optimization technique
title_sort enhancing extreme learning machines classification with moth-flame optimization technique
publisher Institute of Advanced Engineering and Science
publishDate 2022
url http://eprints.utm.my/id/eprint/98678/1/RoselinaSallehuddin2022_EnhancingExtremeLearningMachines.pdf
http://eprints.utm.my/id/eprint/98678/
http://dx.doi.org/10.11591/ijeecs.v26.i2.pp1027-1035
_version_ 1756684242588270592
score 13.211869

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