Receiver operating characteristic for diagnosis of wine quality by Bayesian network classifiers

Abstract

This paper is dedicated to demonstrate the use of the receiver operating characteristic (ROC) and the area under the ROC curve (AUC) for diagnosing forecast skill. Several local search heuristic algorithms to discover which one performs better for learning a certain Bayesian networks (BN). Five heuristic search algorithms, including K2, Hill Climbing, Repeated Hill Climber, LAGD Hill Climbing, and TAN, were empirically evaluated and compared. This study tests BN models in a real-world case, the Vinho Verde wine taste preferences. An average AUC of 0.746 and 0.727 respectively in red wine and white wine were obtained by TAN algorithm. The results show that the use of TAN can effectively improve the AUC measures for predicting quality grade.