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Labeled data, particularly for the outlier class, are difficult to obtain. Thus, outlier detection is typically regarded as an unsupervised learning problem. However, it still has an opportunity to obtain few labeled data. For example, a human analyst can give feedback to few data when he/she examines the results of an unsupervised outlier detection method. Moreover, the widely used unsupervised method for outlier detection cannot only take the labeled data into consideration nor use themdoi:10.1109/access.2019.2947736 fatcat:6ylgbl4o25dmtdhdxrp6vra2wa