A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation
With the rapid development of information technology and biomedical engineering, people can get more and more information. At the same time, they begin to study how to apply the advanced technology in biomedical information. The main research of this paper is to optimize the machine learning method...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2018-11-01
|
| Series: | Open Life Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/biol-2018-0044 |
| _version_ | 1819137481587556352 |
|---|---|
| author | Li Mi Zhang Ming Chen Huan Lu Shengfu |
| author_facet | Li Mi Zhang Ming Chen Huan Lu Shengfu |
| author_sort | Li Mi |
| collection | DOAJ |
| description | With the rapid development of information technology and biomedical engineering, people can get more and more information. At the same time, they begin to study how to apply the advanced technology in biomedical information. The main research of this paper is to optimize the machine learning method by particle swarm optimization (PSO) and apply it in the classification of biomedical data. In order to improve the performance of the classification model, we compared the different inertia weight strategies and mutation strategies and their combinations with PSO, and obtained the best inertia weight strategy without mutation, the best mutation strategy without inertia weight and the best combination of the two. Then, we used the three PSO algorithms to optimize the parameters of support vector machine in the classification of biomedical data. We found that the PSO algorithm with the combination of inertia weight and mutation strategy and the inertia weight strategy that we proposed could improve the classification accuracy. This study has an important reference value for the prediction of clinical diseases. |
| first_indexed | 2024-12-22T10:51:34Z |
| format | Article |
| id | doaj.art-c97c2afc53cf4f86bbb71d8dd1be0986 |
| institution | Directory Open Access Journal |
| issn | 2391-5412 |
| language | English |
| last_indexed | 2024-12-22T10:51:34Z |
| publishDate | 2018-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Life Sciences |
| spelling | doaj.art-c97c2afc53cf4f86bbb71d8dd1be09862022-12-21T18:28:46ZengDe GruyterOpen Life Sciences2391-54122018-11-0113135537310.1515/biol-2018-0044biol-2018-0044A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and MutationLi Mi0Zhang Ming1Chen Huan2Lu Shengfu3Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing100124, ChinaDepartment of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing100124, ChinaDepartment of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing100124, ChinaBeijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing100124, ChinaWith the rapid development of information technology and biomedical engineering, people can get more and more information. At the same time, they begin to study how to apply the advanced technology in biomedical information. The main research of this paper is to optimize the machine learning method by particle swarm optimization (PSO) and apply it in the classification of biomedical data. In order to improve the performance of the classification model, we compared the different inertia weight strategies and mutation strategies and their combinations with PSO, and obtained the best inertia weight strategy without mutation, the best mutation strategy without inertia weight and the best combination of the two. Then, we used the three PSO algorithms to optimize the parameters of support vector machine in the classification of biomedical data. We found that the PSO algorithm with the combination of inertia weight and mutation strategy and the inertia weight strategy that we proposed could improve the classification accuracy. This study has an important reference value for the prediction of clinical diseases.https://doi.org/10.1515/biol-2018-0044biomedical information classificationsupport vector machineparticle swarm optimizationinertia weight strategymutation strategy |
| spellingShingle | Li Mi Zhang Ming Chen Huan Lu Shengfu A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation Open Life Sciences biomedical information classification support vector machine particle swarm optimization inertia weight strategy mutation strategy |
| title | A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation |
| title_full | A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation |
| title_fullStr | A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation |
| title_full_unstemmed | A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation |
| title_short | A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation |
| title_sort | method of biomedical information classification based on particle swarm optimization with inertia weight and mutation |
| topic | biomedical information classification support vector machine particle swarm optimization inertia weight strategy mutation strategy |
| url | https://doi.org/10.1515/biol-2018-0044 |
| work_keys_str_mv | AT limi amethodofbiomedicalinformationclassificationbasedonparticleswarmoptimizationwithinertiaweightandmutation AT zhangming amethodofbiomedicalinformationclassificationbasedonparticleswarmoptimizationwithinertiaweightandmutation AT chenhuan amethodofbiomedicalinformationclassificationbasedonparticleswarmoptimizationwithinertiaweightandmutation AT lushengfu amethodofbiomedicalinformationclassificationbasedonparticleswarmoptimizationwithinertiaweightandmutation AT limi methodofbiomedicalinformationclassificationbasedonparticleswarmoptimizationwithinertiaweightandmutation AT zhangming methodofbiomedicalinformationclassificationbasedonparticleswarmoptimizationwithinertiaweightandmutation AT chenhuan methodofbiomedicalinformationclassificationbasedonparticleswarmoptimizationwithinertiaweightandmutation AT lushengfu methodofbiomedicalinformationclassificationbasedonparticleswarmoptimizationwithinertiaweightandmutation |