期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 115, 期 -, 页码 593-606出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2018.08.042
关键词
Machine learning; K nearest neighbours; Ion-mobility spectrometry; Scent classification
类别
资金
- Academy of Finland [295432, 295433, 295434]
- Finnish Cultural Foundation
- Academy of Finland (AKA) [295434, 295433, 295432, 295432, 295434, 295433] Funding Source: Academy of Finland (AKA)
Various classifiers for scent classification based on measurements using an electronic nose (eNose) have been studied recently. In general, classifiers rely on a static database containing reference eNose measurements for known scents. However, most of these approaches require retraining of the classifier every time a new scent needs to be added to the training database. In this paper, the potential of a K nearest neighbors (KNN) classifier is investigated to avoid the time-consuming retraining when updating the database. To speed up classification, a k-dimensional tree search in the KNN classifier and principal component analysis (PCA) are studied. The tests with scents presented to an eNose based on ion-mobility spectrometry (IMS) show that the KNN method classifies scents with high accuracy. Using a k-dimensional tree search instead of an exhaustive search has no significant influence on the misclassification rate but reduces the classification time considerably. The use of PCA-transformed data results in a higher misclassification rate than the use of IMS data when only the first principal components explaining 95% of the total variance are used but in a similar misclassification rate when the first principal components explaining 99% of the total variance are used. In conclusion, the proposed method can be recommended for classifying scents measured with IMS-based eNoses. (C) 2018 Elsevier Ltd. All rights reserved.
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