Quantitative Comparison of Tree Ensemble Learning Methods for Perfume Identification Using a Portable Electronic Nose
Perfume identification (PI) based on an electronic nose (EN) can be used for exposing counterfeit perfumes more time-efficiently and cost-effectively than using gas chromatography and mass spectrometry instruments. During the past five years, decision-tree-based ensemble learning methods, also calle...
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9716 |
| _version_ | 1797480652598149120 |
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| author | Mengli Cao Xingwei Ling |
| author_facet | Mengli Cao Xingwei Ling |
| author_sort | Mengli Cao |
| collection | DOAJ |
| description | Perfume identification (PI) based on an electronic nose (EN) can be used for exposing counterfeit perfumes more time-efficiently and cost-effectively than using gas chromatography and mass spectrometry instruments. During the past five years, decision-tree-based ensemble learning methods, also called tree ensemble learning methods, have demonstrated excellent performance when solving multi-class classification problems. However, the performance of tree ensemble learning methods for the EN-based PI problem remains uncertain. In this paper, four well-known tree ensemble learning classification methods, random forest (RF), stagewise additive modeling using a multi-class exponential loss function (SAMME), gradient-boosting decision tree (GBDT), and extreme gradient boosting (XGBoost), were implemented for PI using our self-designed EN. For fair comparison, all the tested classification methods used as input the same feature data extracted using principal component analysis. Moreover, two benchmark methods, neural network and support vector machine, were also tested with the same experimental setup. The quantitative results of experiments undertaken demonstrated that the mean PI accuracy achieved by XGBoost was up to 97.5%, and that XGBoost outperformed other tested methods in terms of accuracy mean and variance based on our self-designed EN. |
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| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:04:07Z |
| publishDate | 2022-09-01 |
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| record_format | Article |
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| spelling | doaj.art-02234db121864690a06dbf9a537d3c4e2023-11-23T19:44:22ZengMDPI AGApplied Sciences2076-34172022-09-011219971610.3390/app12199716Quantitative Comparison of Tree Ensemble Learning Methods for Perfume Identification Using a Portable Electronic NoseMengli Cao0Xingwei Ling1Logistic Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaLogistic Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaPerfume identification (PI) based on an electronic nose (EN) can be used for exposing counterfeit perfumes more time-efficiently and cost-effectively than using gas chromatography and mass spectrometry instruments. During the past five years, decision-tree-based ensemble learning methods, also called tree ensemble learning methods, have demonstrated excellent performance when solving multi-class classification problems. However, the performance of tree ensemble learning methods for the EN-based PI problem remains uncertain. In this paper, four well-known tree ensemble learning classification methods, random forest (RF), stagewise additive modeling using a multi-class exponential loss function (SAMME), gradient-boosting decision tree (GBDT), and extreme gradient boosting (XGBoost), were implemented for PI using our self-designed EN. For fair comparison, all the tested classification methods used as input the same feature data extracted using principal component analysis. Moreover, two benchmark methods, neural network and support vector machine, were also tested with the same experimental setup. The quantitative results of experiments undertaken demonstrated that the mean PI accuracy achieved by XGBoost was up to 97.5%, and that XGBoost outperformed other tested methods in terms of accuracy mean and variance based on our self-designed EN.https://www.mdpi.com/2076-3417/12/19/9716electronic noseperfume identificationensemble learningXGBoost |
| spellingShingle | Mengli Cao Xingwei Ling Quantitative Comparison of Tree Ensemble Learning Methods for Perfume Identification Using a Portable Electronic Nose Applied Sciences electronic nose perfume identification ensemble learning XGBoost |
| title | Quantitative Comparison of Tree Ensemble Learning Methods for Perfume Identification Using a Portable Electronic Nose |
| title_full | Quantitative Comparison of Tree Ensemble Learning Methods for Perfume Identification Using a Portable Electronic Nose |
| title_fullStr | Quantitative Comparison of Tree Ensemble Learning Methods for Perfume Identification Using a Portable Electronic Nose |
| title_full_unstemmed | Quantitative Comparison of Tree Ensemble Learning Methods for Perfume Identification Using a Portable Electronic Nose |
| title_short | Quantitative Comparison of Tree Ensemble Learning Methods for Perfume Identification Using a Portable Electronic Nose |
| title_sort | quantitative comparison of tree ensemble learning methods for perfume identification using a portable electronic nose |
| topic | electronic nose perfume identification ensemble learning XGBoost |
| url | https://www.mdpi.com/2076-3417/12/19/9716 |
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