Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose.
As the precise odor-sensing equipment, the electronic nose integrates multiple advanced and sensitive sensors that can identify wound infections non-invasively and rapidly by analyzing wound characteristic odor. To reduce the cost of sensors and improve or maintain e-nose's performance, efficie...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327748 |
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| _version_ | 1849430545423925248 |
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| author | Jia Liu Jinglei Zhang Shaoqi Zhang Kaiwei Li Xiang Li Shuo Zhang Hang Gu Zhen Chen Chao Liu Nan Zhang Tong Sun |
| author_facet | Jia Liu Jinglei Zhang Shaoqi Zhang Kaiwei Li Xiang Li Shuo Zhang Hang Gu Zhen Chen Chao Liu Nan Zhang Tong Sun |
| author_sort | Jia Liu |
| collection | DOAJ |
| description | As the precise odor-sensing equipment, the electronic nose integrates multiple advanced and sensitive sensors that can identify wound infections non-invasively and rapidly by analyzing wound characteristic odor. To reduce the cost of sensors and improve or maintain e-nose's performance, efficient optimization of sensor arrays is required. For this issue, we proposed a new sensor array optimization algorithm named Interfered Feature Elimination coupled with Feature Group Selection (IFE-FGS). In this method, the IFE algorithm first removed the bad sensor features; then the FGS algorithm determined the optimized sensor combination by gradually selecting the features in groups. The experimental results show the superiority of the IFE- FGS method on two bacteria datasets and six public gene expression profiling datasets. IFE- FGS achieves the classification accuracy of 93.95% and 94.94% in mean accuracy and max accuracy, respectively, on the bacteria dataset 2, which is significantly ahead of the comparison methods. Besides, our proposed method shows consistency and effectiveness. It achieves excellent performance, which takes two first-places, four second-places in mean accuracy, one first-place, and six second-places in max accuracy. Moreover, it also explores three novel and valuable discoveries for the electronic nose: 1) It can effectively identify biomarkers in the application. 2) It can effectively distinguish the degree of chemical components contributing to odors. 3) It can reveal the effective detection range of the targets. |
| format | Article |
| id | doaj-art-ae356bde628641e7b6096f32bdd7c467 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-ae356bde628641e7b6096f32bdd7c4672025-08-20T03:27:57ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032774810.1371/journal.pone.0327748Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose.Jia LiuJinglei ZhangShaoqi ZhangKaiwei LiXiang LiShuo ZhangHang GuZhen ChenChao LiuNan ZhangTong SunAs the precise odor-sensing equipment, the electronic nose integrates multiple advanced and sensitive sensors that can identify wound infections non-invasively and rapidly by analyzing wound characteristic odor. To reduce the cost of sensors and improve or maintain e-nose's performance, efficient optimization of sensor arrays is required. For this issue, we proposed a new sensor array optimization algorithm named Interfered Feature Elimination coupled with Feature Group Selection (IFE-FGS). In this method, the IFE algorithm first removed the bad sensor features; then the FGS algorithm determined the optimized sensor combination by gradually selecting the features in groups. The experimental results show the superiority of the IFE- FGS method on two bacteria datasets and six public gene expression profiling datasets. IFE- FGS achieves the classification accuracy of 93.95% and 94.94% in mean accuracy and max accuracy, respectively, on the bacteria dataset 2, which is significantly ahead of the comparison methods. Besides, our proposed method shows consistency and effectiveness. It achieves excellent performance, which takes two first-places, four second-places in mean accuracy, one first-place, and six second-places in max accuracy. Moreover, it also explores three novel and valuable discoveries for the electronic nose: 1) It can effectively identify biomarkers in the application. 2) It can effectively distinguish the degree of chemical components contributing to odors. 3) It can reveal the effective detection range of the targets.https://doi.org/10.1371/journal.pone.0327748 |
| spellingShingle | Jia Liu Jinglei Zhang Shaoqi Zhang Kaiwei Li Xiang Li Shuo Zhang Hang Gu Zhen Chen Chao Liu Nan Zhang Tong Sun Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose. PLoS ONE |
| title | Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose. |
| title_full | Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose. |
| title_fullStr | Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose. |
| title_full_unstemmed | Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose. |
| title_short | Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose. |
| title_sort | interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose |
| url | https://doi.org/10.1371/journal.pone.0327748 |
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