Conformal Prediction Based on Raman Spectra for the Classification of Chinese Liquors

Abstract

This work extends the conventional back-propagation neural network (BPNN) to the classification of Chinese liquors of different flavors according to their Raman spectra. Conformal prediction is applied to assign reliable confidence measures for each classification and support an effective framework to make the machine learning on classification trustable. The BPNN can be used to predict the flavors of Chinese liquors according to their Raman spectra, and a classification rate of 88.96% can be achieved. In order to evaluate each classification, a non-conformity score is defined to generate a P-value for each classification. Moreover, the validity of conformal prediction in online mode is discussed. The number of cumulative errors in the conformal prediction is much less than that without conformal prediction. The relationship between the cumulative error and confidence levels shows that a high confidence level leads to low cumulative errors, but many cumulative errors will occur under a very high confidence level. The result implies that conformal prediction is a useful framework, which can employ classification satisfying a certain level of confidence. Meanwhile, the conformal prediction can improve our classification using a BPNN, when the number of data points is limited.