  A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient
Boosting Outlier Detection) is proposed, described and demonstrated for the
enhanced detection of outliers from normal observations in various practical
datasets. The proposed framework combines the strengths of both supervised and
unsupervised machine learning methods by creating a hybrid approach that
exploits each of their individual performance capabilities in outlier
detection. XGBOD uses multiple unsupervised outlier mining algorithms to
extract useful representations from the underlying data that augment the
predictive capabilities of an embedded supervised classifier on an improved
feature space. The novel approach is shown to provide superior performance in
comparison to competing individual detectors, the full ensemble and two
existing representation learning based algorithms across seven outlier
datasets.
