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.
Highlights
Two main problems that the electronic nose (E-nose) faces are classification and regression.Lots of techniques, such as support vector machine (SVM) [1], k-nearest neighbors (KNN) [2,3,4], artificial neural network (ANN) [5,6], linear discriminant analysis (LDA) [2,3,4,7,8] and other methodologies, have been successfully applied for predictions with E-nose
Many approaches have been developed to complement these drawbacks by predicting with additional information, such as probably approximately correct learning (PAC), Bayesian learning, generalized least square regression in combination with a stepwise backward selection [9] and hold-out estimate or cross-validation
Every ginseng sample was pulverized into powder, and 10 g powder of every sample was put into 100 mL empty glass bottles, which has been washed with clean air for 30 min separately before
Summary
Two main problems that the electronic nose (E-nose) faces are classification and regression.Lots of techniques, such as support vector machine (SVM) [1], k-nearest neighbors (KNN) [2,3,4], artificial neural network (ANN) [5,6], linear discriminant analysis (LDA) [2,3,4,7,8] and other methodologies, have been successfully applied for predictions with E-nose. Two main problems that the electronic nose (E-nose) faces are classification and regression. Many approaches have been developed to complement these drawbacks by predicting with additional information, such as probably approximately correct learning (PAC), Bayesian learning, generalized least square regression in combination with a stepwise backward selection [9] and hold-out estimate or cross-validation. PAC learning does not provide any information on the reliability of individual prediction [10]. Bayesian learning and other probability algorithms, such as logistic regression [11] and Platt’s method [12], can complement every individual prediction with information of probability to indicate how every potential label is correct.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
