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Sensors 2017, 17(8), 1869;

Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose

State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, China
Computer Learning Research Centre, Royal Holloway, University of London, Egham Hill, Egham, Surrey TW20 0EX, UK
Author to whom correspondence should be addressed.
Received: 8 July 2017 / Revised: 2 August 2017 / Accepted: 8 August 2017 / Published: 14 August 2017
(This article belongs to the Special Issue Electronic Tongues and Electronic Noses)
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An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of conformal prediction. In offline mode, the conformal predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity. View Full-Text
Keywords: conformal prediction; electronic nose; k-nearest neighbors; ginseng conformal prediction; electronic nose; k-nearest neighbors; ginseng

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Wang, Z.; Sun, X.; Miao, J.; Wang, Y.; Luo, Z.; Li, G. Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose. Sensors 2017, 17, 1869.

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