Y. Zhao and M.K. Hryniewicki, "XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning," International Joint Conference on Neural Networks (IJCNN), IEEE, 2018. Accepted, to appear. XGBOD is a three-phase framework (see Figure below). In the first phase, it generates new data representations. Specifically, various unsupervised outlier detection methods are applied to the original data to get transformed outlier scores as new data representations. In the second phase, a selection process is performed on newly generated outlier scores to keep the useful ones. The selected outlier scores are then combined with the original features to become the new feature space. Finally, an XGBoost model is trained on the new feature space, and its output decides the outlier prediction result.