Search Results for author:
Found 3 papers, 3 papers with code
XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning
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.
DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles
Selecting and combining the outlier scores of different base detectors used within outlier ensembles can be quite challenging in the absence of ground truth.
LSCP: Locally Selective Combination in Parallel Outlier Ensembles
The top-performing base detectors in this local region are selected and combined as the model's final output.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
