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

In this paper, the problem of classifying manufacturing process conditions into normal and defective products according to defect types is dealt with and 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.