A Classification Framework Using Imperfectly Labeled Data for Manufacturing Applications
In recent years, classification techniques have been broadly adopted for a variety of smart manufacturing applications, including system health maintenance, defect detection and diagnosis, etc. However, most classification methods require a large set of training data that are accurately labeled by human experts in a specific domain. Collecting these training data is time-consuming and prohibitively expensive in practical applications. To overcome this challenge, we develop a classification framework using imperfectly labeled data. First, a statistical model is proposed to derive a set of probabilistic labels with consideration of labeling errors. Next, an accurate classifier is trained from these inaccurate labels. As demonstrated by the experimental results of two industrial examples, the proposed framework achieves superior classification accuracy over other conventional approaches.
