A Hybrid of Shallow and Deep Learning for Odor Classification Based on Adaptive Boosting

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dc.contributor.authorGrodniyomchai, Boonyawee
dc.contributor.authorChalapat, Khattiya
dc.contributor.authorJitkajornwanich, Kulsawasd
dc.contributor.authorJaiyen, Saichon
dc.date.accessioned2026-08-06T10:26:08Z
dc.date.available2026-08-06T10:26:08Z
dc.date.issued2019-11-01
dc.description.abstractAn electronic nose is very useful for identifying an odor that is harmful to humans. To get the most accurate odor predictions from an electronic nose, we combined the models of traditional machine learning and deep learning, including deep neural network (DNN), support vector machine (SVM) and decision tree, to make a new hybrid model that adopts the AdaBoost algorithm to adjust the weights of weak classifiers to build a strong classifier using odor data. Experimental results from our model were compared with other models, including a single deep neural network, an ensemble of SVM models and an ensemble of decision trees. Our model achieved an averaged accuracy of 99.58%, which is better than other models, and the standard deviation, 0.67%, is also less than other models.
dc.identifier.citationProceedings 9th IEEE International Conference on Control System Computing and Engineering Iccsce 2019, 61-65, 2019
dc.identifier.doi10.1109/ICCSCE47578.2019.9068552
dc.identifier.other2-s2.0-85084302390
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/10299
dc.sourceProceedings 9th IEEE International Conference on Control System Computing and Engineering Iccsce 2019
dc.subjectartificial intelligence
dc.subjectdeep neural network
dc.subjectelectronic nose
dc.subjectensemble learning
dc.subjectmachine learning
dc.subjectodor classification
dc.titleA Hybrid of Shallow and Deep Learning for Odor Classification Based on Adaptive Boosting
dc.typeConference Paper

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