Artificial Odour Classification System
This chapter overviews the issue of multicollinearity in electronic nose (e-nose) classification and investigates some analytical solutions to deal with the problem. Multicollinearity effect may harm classification analysis from producing good parameters estimate during the construction of the class...
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| Format: | Book Section |
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IGI Global
2018
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| _version_ | 1825805081187975168 |
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| author | Mahat, Nor Idayu Masnan, Maz Jamilah Shakaff, Ali Yeon Md Zakaria, Ammar Abdul Kadir, Muhd Khairulzaman |
| author2 | Albastaki, Yousif |
| author_facet | Albastaki, Yousif Mahat, Nor Idayu Masnan, Maz Jamilah Shakaff, Ali Yeon Md Zakaria, Ammar Abdul Kadir, Muhd Khairulzaman |
| author_sort | Mahat, Nor Idayu |
| collection | UUM |
| description | This chapter overviews the issue of multicollinearity in electronic nose (e-nose) classification and investigates some analytical solutions to deal with the problem. Multicollinearity effect may harm classification analysis from producing good parameters estimate during the construction of the classification rule.The common approach to deal with multicollinearity is feature extraction.However, the criterion used in extracting the raw features based on variances may not be appropriate for the ultimate goal of classification accuracy. Alternatively, feature selection method would be advisable as it chooses only valuable features. Two distance-based criteria in determining the right features for classification purposes, Wilk's Lambda and bounded Mahalanobis distance, are applied. Classification with features determined by bounded Mahalanobis distance statistically performs better than Wilk's Lambda.This chapter suggests that classification of e-nose with feature selection is a good choice to limit the cost of experiments and maintain good classification performance. |
| first_indexed | 2024-07-04T06:26:03Z |
| format | Book Section |
| id | uum-24302 |
| institution | Universiti Utara Malaysia |
| last_indexed | 2024-07-04T06:26:03Z |
| publishDate | 2018 |
| publisher | IGI Global |
| record_format | eprints |
| spelling | uum-243022018-06-25T01:50:23Z https://repo.uum.edu.my/id/eprint/24302/ Artificial Odour Classification System Mahat, Nor Idayu Masnan, Maz Jamilah Shakaff, Ali Yeon Md Zakaria, Ammar Abdul Kadir, Muhd Khairulzaman QA75 Electronic computers. Computer science This chapter overviews the issue of multicollinearity in electronic nose (e-nose) classification and investigates some analytical solutions to deal with the problem. Multicollinearity effect may harm classification analysis from producing good parameters estimate during the construction of the classification rule.The common approach to deal with multicollinearity is feature extraction.However, the criterion used in extracting the raw features based on variances may not be appropriate for the ultimate goal of classification accuracy. Alternatively, feature selection method would be advisable as it chooses only valuable features. Two distance-based criteria in determining the right features for classification purposes, Wilk's Lambda and bounded Mahalanobis distance, are applied. Classification with features determined by bounded Mahalanobis distance statistically performs better than Wilk's Lambda.This chapter suggests that classification of e-nose with feature selection is a good choice to limit the cost of experiments and maintain good classification performance. IGI Global Albastaki, Yousif Albalooshi, Fatema 2018 Book Section PeerReviewed Mahat, Nor Idayu and Masnan, Maz Jamilah and Shakaff, Ali Yeon Md and Zakaria, Ammar and Abdul Kadir, Muhd Khairulzaman (2018) Artificial Odour Classification System. In: Electronic Nose Technologies and Advances in Machine Olfaction. IGI Global, pp. 25-37. ISBN 9781522538622 http://doi.org/10.4018/978-1-5225-3862-2.ch002 doi:10.4018/978-1-5225-3862-2.ch002 doi:10.4018/978-1-5225-3862-2.ch002 |
| spellingShingle | QA75 Electronic computers. Computer science Mahat, Nor Idayu Masnan, Maz Jamilah Shakaff, Ali Yeon Md Zakaria, Ammar Abdul Kadir, Muhd Khairulzaman Artificial Odour Classification System |
| title | Artificial Odour Classification System |
| title_full | Artificial Odour Classification System |
| title_fullStr | Artificial Odour Classification System |
| title_full_unstemmed | Artificial Odour Classification System |
| title_short | Artificial Odour Classification System |
| title_sort | artificial odour classification system |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT mahatnoridayu artificialodourclassificationsystem AT masnanmazjamilah artificialodourclassificationsystem AT shakaffaliyeonmd artificialodourclassificationsystem AT zakariaammar artificialodourclassificationsystem AT abdulkadirmuhdkhairulzaman artificialodourclassificationsystem |