Process-monitoring-for-quality—A robust model selection criterion for the logistic regression algorithm

Abstract

Abstract Process Monitoring for Quality is a big data-driven quality philosophy aimed at defect detection through binary classification. The l 1 -regularized Logistic Regression learning algorithm has been successfully applied in manufacturing systems for rare quality event detection. Since the optimal value of the regularization parameter is not known in advance, many models should be created and tested to find the final model to be deployed at the plant. In this context, model selection becomes a critical step in the process of developing a manufacturing functional model. Since most mature organizations generate only a few Defects Per Million of Opportunities, a three-dimensional model selection criterion ( 3 D - LR ) was initially introduced aimed at analyzing highly/ultra unbalanced binary data structures. The 3 D - LR criterion combines three of the most important attributes – prediction, separability, complexity – of each candidate model and map them into a three dimensional space to select the best one. In this letter, the 3 D - LR is improved; the fit attribute is replaced by a novel separability index that takes into consideration the classification threshold to reward for robustness of predictions. Updated criterion, 3 D - LRI , is an improved version of the initial concept.