A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections
An electronic nose (E-nose) consisting of 14 metal oxide gas sensors and one electronic chemical gas sensor has been constructed to identify four different classes of wound infection. However, the classification results of the E-nose are not ideal if the original feature matrix containing the maximu...
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MDPI AG
2016-06-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/7/1019 |
| _version_ | 1798042192806150144 |
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| author | Pengfei Jia Tailai Huang Li Wang Shukai Duan Jia Yan Lidan Wang |
| author_facet | Pengfei Jia Tailai Huang Li Wang Shukai Duan Jia Yan Lidan Wang |
| author_sort | Pengfei Jia |
| collection | DOAJ |
| description | An electronic nose (E-nose) consisting of 14 metal oxide gas sensors and one electronic chemical gas sensor has been constructed to identify four different classes of wound infection. However, the classification results of the E-nose are not ideal if the original feature matrix containing the maximum steady-state response value of sensors is processed by the classifier directly, so a novel pre-processing technique based on supervised locality preserving projections (SLPP) is proposed in this paper to process the original feature matrix before it is put into the classifier to improve the performance of the E-nose. SLPP is good at finding and keeping the nonlinear structure of data; furthermore, it can provide an explicit mapping expression which is unreachable by the traditional manifold learning methods. Additionally, some effective optimization methods are found by us to optimize the parameters of SLPP and the classifier. Experimental results prove that the classification accuracy of support vector machine (SVM combined with the data pre-processed by SLPP outperforms other considered methods. All results make it clear that SLPP has a better performance in processing the original feature matrix of the E-nose. |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:32:03Z |
| publishDate | 2016-06-01 |
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| spelling | doaj.art-6b580b7704a24a2b8277d2a2213971e72022-12-22T03:59:20ZengMDPI AGSensors1424-82202016-06-01167101910.3390/s16071019s16071019A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving ProjectionsPengfei Jia0Tailai Huang1Li Wang2Shukai Duan3Jia Yan4Lidan Wang5College of Electronic and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing 400715, ChinaAn electronic nose (E-nose) consisting of 14 metal oxide gas sensors and one electronic chemical gas sensor has been constructed to identify four different classes of wound infection. However, the classification results of the E-nose are not ideal if the original feature matrix containing the maximum steady-state response value of sensors is processed by the classifier directly, so a novel pre-processing technique based on supervised locality preserving projections (SLPP) is proposed in this paper to process the original feature matrix before it is put into the classifier to improve the performance of the E-nose. SLPP is good at finding and keeping the nonlinear structure of data; furthermore, it can provide an explicit mapping expression which is unreachable by the traditional manifold learning methods. Additionally, some effective optimization methods are found by us to optimize the parameters of SLPP and the classifier. Experimental results prove that the classification accuracy of support vector machine (SVM combined with the data pre-processed by SLPP outperforms other considered methods. All results make it clear that SLPP has a better performance in processing the original feature matrix of the E-nose.http://www.mdpi.com/1424-8220/16/7/1019sensor dataelectronic noseSLPPdata pre-processingwound infection |
| spellingShingle | Pengfei Jia Tailai Huang Li Wang Shukai Duan Jia Yan Lidan Wang A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections Sensors sensor data electronic nose SLPP data pre-processing wound infection |
| title | A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections |
| title_full | A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections |
| title_fullStr | A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections |
| title_full_unstemmed | A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections |
| title_short | A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections |
| title_sort | novel pre processing technique for original feature matrix of electronic nose based on supervised locality preserving projections |
| topic | sensor data electronic nose SLPP data pre-processing wound infection |
| url | http://www.mdpi.com/1424-8220/16/7/1019 |
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