A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems
The increasingly serious haze problem in China has brought about a growing public awareness of air quality. Precise air quality index (AQI) forecasts play an important role in both controlling air pollution and promoting the sustainable development of human society. However, the randomness, non-stat...
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doaj-b4779e2bca924970b05c9baf44637c592021-03-29T22:30:42ZengIEEEIEEE Access2169-35362019-01-017277892780110.1109/ACCESS.2019.28976448635462A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification ProblemsHongxin Xue0https://orcid.org/0000-0002-8871-6438Yanping Bai1https://orcid.org/0000-0002-2043-8363Hongping Hu2Ting Xu3Haijian Liang4School of Information and Communication Engineering, North University of China, Taiyuan, ChinaDepartment of Mathematics, School of Science, North University of China, Taiyuan, ChinaDepartment of Mathematics, School of Science, North University of China, Taiyuan, ChinaDepartment of Mathematics, School of Science, North University of China, Taiyuan, ChinaNational Key Laboratory for Electronic Measurement Technology, Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of Educations, School of Information and Communication Engineering, North University of China, Taiyuan, ChinaThe increasingly serious haze problem in China has brought about a growing public awareness of air quality. Precise air quality index (AQI) forecasts play an important role in both controlling air pollution and promoting the sustainable development of human society. However, the randomness, non-stationarity, and irregularity of the AQI series make its classifications very difficult. This paper introduces a time-varying inertia weighting (TVIW) strategy based on a combination of gravitation search algorithm (GSA) and particle swarm optimization (PSO) called the TVIW-PSO-GSA. The TVIW-PSO-GSA is utilized to optimize the penalty parameter C and kernel function parameter γ of a support vector machine (SVM) to create a hybrid TVIW-PSO-GSA-SVM algorithm. Twenty-three benchmark functions, five UCI datasets, and an AQI hierarchical classification example are tested to find that the convergence speed and performance of the TVI-PSO-GSA exceed those of other algorithms, and the TVIW-PSO-GSA-SVM algorithm also achieves higher classification accuracy and efficiency than the PSO-GSA-SVM, GSA-SVM, GA-SVM, or PSO-SVM, which indicates that the TVIW-PSO-GSA-SVM reliably and accurately classifies AQI and UCI datasets.https://ieeexplore.ieee.org/document/8635462/Intelligent optimization algorithmGSAPSOtime-varying inertia weighting strategySVM |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hongxin Xue Yanping Bai Hongping Hu Ting Xu Haijian Liang |
spellingShingle |
Hongxin Xue Yanping Bai Hongping Hu Ting Xu Haijian Liang A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems IEEE Access Intelligent optimization algorithm GSA PSO time-varying inertia weighting strategy SVM |
author_facet |
Hongxin Xue Yanping Bai Hongping Hu Ting Xu Haijian Liang |
author_sort |
Hongxin Xue |
title |
A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems |
title_short |
A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems |
title_full |
A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems |
title_fullStr |
A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems |
title_full_unstemmed |
A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems |
title_sort |
novel hybrid model based on tviw-pso-gsa algorithm and support vector machine for classification problems |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
The increasingly serious haze problem in China has brought about a growing public awareness of air quality. Precise air quality index (AQI) forecasts play an important role in both controlling air pollution and promoting the sustainable development of human society. However, the randomness, non-stationarity, and irregularity of the AQI series make its classifications very difficult. This paper introduces a time-varying inertia weighting (TVIW) strategy based on a combination of gravitation search algorithm (GSA) and particle swarm optimization (PSO) called the TVIW-PSO-GSA. The TVIW-PSO-GSA is utilized to optimize the penalty parameter C and kernel function parameter γ of a support vector machine (SVM) to create a hybrid TVIW-PSO-GSA-SVM algorithm. Twenty-three benchmark functions, five UCI datasets, and an AQI hierarchical classification example are tested to find that the convergence speed and performance of the TVI-PSO-GSA exceed those of other algorithms, and the TVIW-PSO-GSA-SVM algorithm also achieves higher classification accuracy and efficiency than the PSO-GSA-SVM, GSA-SVM, GA-SVM, or PSO-SVM, which indicates that the TVIW-PSO-GSA-SVM reliably and accurately classifies AQI and UCI datasets. |
topic |
Intelligent optimization algorithm GSA PSO time-varying inertia weighting strategy SVM |
url |
https://ieeexplore.ieee.org/document/8635462/ |
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