ArticleSensors (Basel, Switzerland)2017
Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley-Sammon transformation.Journal of food science and technology · 2020Article
- Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor.Sensors (Basel, Switzerland) · 2019Article
- Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose.Sensors (Basel, Switzerland) · 2018Article
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6 authors.
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Abstract
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.
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