CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose
Published in November, 2021
A new conformal prediction framework with shrunken centroids (CPSC) is proposed. It regularizes the class centroids to attenuate the irrelevant features and shrink the sample space for predictions and reliability quantification. We compared the CPSC with CP based on KNN, SVM, LightGBM, RF on the herbal medicine dataset, with the electronic nose as a case and assessed them in two tasks, off-line prediction and online prediction with data augmentation. The result shows that CPSC reached a significantly higher accuracy with lower computation cost, and with the same credibility output, CPSC generally achieved higher accuracy. The data augmentation process with CPSC robustly manifested a statistically significant improvement in prediction accuracy with different reliability thresholds, and the augmented data were more balanced in classes.