Reference Hub1
Artificial Odour Classification System

Artificial Odour Classification System

Nor Idayu Mahat, Maz Jamilah Masnan, Ali Yeon Md Shakaff, Ammar Zakaria, Muhd Khairulzaman Abdul Kadir
Copyright: © 2018 |Pages: 13
ISBN13: 9781522538622|ISBN10: 1522538623|EISBN13: 9781522538639
DOI: 10.4018/978-1-5225-3862-2.ch002
Cite Chapter Cite Chapter

MLA

Mahat, Nor Idayu, et al. "Artificial Odour Classification System." Electronic Nose Technologies and Advances in Machine Olfaction, edited by Yousif Abdullatif Albastaki and Fatema Albalooshi, IGI Global, 2018, pp. 25-37. https://doi.org/10.4018/978-1-5225-3862-2.ch002

APA

Mahat, N. I., Masnan, M. J., Shakaff, A. Y., Zakaria, A., & Kadir, M. K. (2018). Artificial Odour Classification System. In Y. Albastaki & F. Albalooshi (Eds.), Electronic Nose Technologies and Advances in Machine Olfaction (pp. 25-37). IGI Global. https://doi.org/10.4018/978-1-5225-3862-2.ch002

Chicago

Mahat, Nor Idayu, et al. "Artificial Odour Classification System." In Electronic Nose Technologies and Advances in Machine Olfaction, edited by Yousif Abdullatif Albastaki and Fatema Albalooshi, 25-37. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3862-2.ch002

Export Reference

Mendeley
Favorite

Abstract

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.