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A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach
A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492859/ https://www.ncbi.nlm.nih.gov/pubmed/28629202 http://dx.doi.org/10.3390/s17061434 |
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author | Jian, Yulin Huang, Daoyu Yan, Jia Lu, Kun Huang, Ying Wen, Tailai Zeng, Tanyue Zhong, Shijie Xie, Qilong |
author_facet | Jian, Yulin Huang, Daoyu Yan, Jia Lu, Kun Huang, Ying Wen, Tailai Zeng, Tanyue Zhong, Shijie Xie, Qilong |
author_sort | Jian, Yulin |
collection | PubMed |
description | A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. |
format | Online Article Text |
id | pubmed-5492859 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-54928592017-07-03 A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach Jian, Yulin Huang, Daoyu Yan, Jia Lu, Kun Huang, Ying Wen, Tailai Zeng, Tanyue Zhong, Shijie Xie, Qilong Sensors (Basel) Article A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. MDPI 2017-06-19 /pmc/articles/PMC5492859/ /pubmed/28629202 http://dx.doi.org/10.3390/s17061434 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jian, Yulin Huang, Daoyu Yan, Jia Lu, Kun Huang, Ying Wen, Tailai Zeng, Tanyue Zhong, Shijie Xie, Qilong A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach |
title | A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach |
title_full | A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach |
title_fullStr | A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach |
title_full_unstemmed | A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach |
title_short | A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach |
title_sort | novel extreme learning machine classification model for e-nose application based on the multiple kernel approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492859/ https://www.ncbi.nlm.nih.gov/pubmed/28629202 http://dx.doi.org/10.3390/s17061434 |
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