Imbalanced classification of manufacturing quality conditions using cost-sensitive decision tree ensembles

Data-driven quality control techniques are being actively developed for implementation in smart factories. Quality prediction during manufacturing processes is a good example of how big data analytics can influence advanced manufacturing environments. In this paper, the problem of classifying manufacturing process conditions into normal and defective products according to defect types is dealt with. Such a quality analysis data set is generally unbalanced because the defective rate is quite low in practice. To solve this imbalanced classification problem, a cost-sensitive decision tree ensemble algorithm is adopted to boost the small number of defective cases and assign a higher cost to the misclassification of defective products than that of normal products. C4.5 decision trees are used as base classifiers, and three cost-sensitive ensembles, AdaC1, AdaC2 and AdaC3, are tried to address the imbalanced classification. A few types of defect conditions in a real-world die-casting data set were predicted through the proposed methods. In these experiments, the cost-sensitive ensembles were able to classify the imbalanced data and detect the defect conditions more precisely and more exactly than 19 algorithms in other classification categories such as classic classifiers and ensembles, cost-sensitive single classifiers and sampling-based ensembles. Especially, the AdaC2-based method mainly outperformed all other classification algorithms in terms of performance measures such as F-measure, G-means and AUC for the die-casting quality condition classification problem.