期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2021
卷号：11
期号：6
页码：4767-4773
DOI：10.11591/ijece.v11i6.pp4767-4773
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：We use electronic nose data of odor measurements to build machine learning classification models. The presented analysis focused on determining the optimal time of measurement, leading to the best model performance. We observe that the most valuable information for classification is available in data collected at the beginning of adsorption and the beginning of the desorption phase of measurement. We demonstrated that the usage of complex features extracted from the sensors’ response gives better classification performance than use as features only raw values of sensors’ response, normalized by baseline. We use a group shuffling cross-validation approach for determining the reported models’ average accuracy and standard deviation.
