Classification model construction of optimized electronic nose system using Naive Bayes

Intelligent models that tackle classification problems have shown that it can be developed using different algorithms that are available to use. Therefore, adding an additional level of complexity during the construction of such models. This paper focused on implementing Naive Bayes classification a...

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Bibliographic Details
Main Authors: Al-Ahdal, Talal Othman Hasan, Zain, Azlan Mohd, Saadan, Zuridah, Zhou, Kai-Qing
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Algorithms
Classification
Electronic noses
Support vector machines
Online Access:Get full text
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Summary:Intelligent models that tackle classification problems have shown that it can be developed using different algorithms that are available to use. Therefore, adding an additional level of complexity during the construction of such models. This paper focused on implementing Naive Bayes classification algorithm to the Optimized Electronic Nose System (OENS). Furthermore, the current implementation of the classification layer in OENS is by the usage of the SVM classification algorithm. This paper implemented and presented the results of the implementation of Naive Bayes algorithm by using the same dataset used during the development of OENS. In addition, the development of the Naive Bayes model was done by the usage of the WEKA tool. Apart from that, this paper served as an experiment to compare the results the Naive Bayes algorithm provided compared to the SVM algorithm used during the development of OENS. The classification model that was developed using Naive Bayes algorithm had an accuracy of 89.7% which is lower than the current accuracy of OENS classification model which is 98.1%. Therefore, the implementation of SVM for this problem has been deemed to be better than Naive Bayes.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0199027