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

Abstract

An 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.