1. A method of outlier detection comprising the steps of: reducing an outlier detection problem to that of a classification learning problem using unlabeled normal data as positive examples and randomly generated synthesized examples as negative examples, and then selectively sampling normal and synthesized examples based on uncertainty of prediction to further reduce an amount of data required for data analysis, resulting in enhanced predictive performance while minimizing computational resources including storage requirements.