Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing
The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extract...
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MDPI AG
2018-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/2/388 |
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doaj-e42be0f398324902a7d25e752c4859a92020-11-24T21:40:04ZengMDPI AGSensors1424-82202018-01-0118238810.3390/s18020388s18020388Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal ProcessingTailai Wen0Jia Yan1Daoyu Huang2Kun Lu3Changjian Deng4Tanyue Zeng5Song Yu6Zhiyi He7College 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, ChinaHigh Tech Department, China International Engineering Consulting Corporation, Beijing 100048, 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, ChinaThe aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.http://www.mdpi.com/1424-8220/18/2/388electronic nosefeature extractionmultiple kernel learningweighted kernels Fisher discriminant analysisclassification |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Tailai Wen Jia Yan Daoyu Huang Kun Lu Changjian Deng Tanyue Zeng Song Yu Zhiyi He |
| spellingShingle |
Tailai Wen Jia Yan Daoyu Huang Kun Lu Changjian Deng Tanyue Zeng Song Yu Zhiyi He Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing Sensors electronic nose feature extraction multiple kernel learning weighted kernels Fisher discriminant analysis classification |
| author_facet |
Tailai Wen Jia Yan Daoyu Huang Kun Lu Changjian Deng Tanyue Zeng Song Yu Zhiyi He |
| author_sort |
Tailai Wen |
| title |
Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing |
| title_short |
Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing |
| title_full |
Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing |
| title_fullStr |
Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing |
| title_full_unstemmed |
Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing |
| title_sort |
feature extraction of electronic nose signals using qpso-based multiple kfda signal processing |
| publisher |
MDPI AG |
| series |
Sensors |
| issn |
1424-8220 |
| publishDate |
2018-01-01 |
| description |
The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods. |
| topic |
electronic nose feature extraction multiple kernel learning weighted kernels Fisher discriminant analysis classification |
| url |
http://www.mdpi.com/1424-8220/18/2/388 |
| work_keys_str_mv |
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