Bayesian optimization for predicting rare internal failures in manufacturing processes

Modern manufacturing processes are highly instrumented to capture data at every step along the assembly lines. Such fine-grained data can help manufacturers perform quality control by predicting which manufactured products are at risk of being defective. A predictive analytics system that identifies internal failures in products before they are shipped can result in significant savings for the manufacturer and result in better customer satisfaction. However, predicting internal failures in manufactured products is a challenging machine learning task due to two reasons: (i) the rarity of such failures in modern manufacturing processes, and (ii) the failure of traditional machine learning algorithms to optimize non-convex metrics such as Matthew's Correlation Coefficient (MCC) used to measure performance on imbalanced datasets. In this paper, we present “ImbalancedBayesOpt”, a meta-optimization algorithm that directly maximizes MCC by learning optimal weights for the positive and negative classes. We use Gradient Boosting Machine (GBM) as the base classifier for our meta-optimization algorithm due to its competitive performance on machine learning prediction tasks. Our quantitative evaluation suggests that “ImbalancedBayesOpt” can significantly improve the classification performance of base classifiers on severely imbalanced high-dimensional datasets.