CARE
Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective
- We propose a sequential ensemble approach called CARE that employs a two-phase aggregation of the intermediate results.
- Our ensemble incorporates both the parallel and sequential building blocks to reduce bias as well as variance by (i) successively eliminating outliers from the original dataset to build a better data model on which outlierness is estimated (sequentially), and (ii) combining the results from individual base detectors and across iterations (parallelly).
- We provide extensive experiments on sixteen real-world datasets, we show that CARE performs significantly better than or at least similar to the individual baselines as well as the existing state-of-the-art outlier ensembles.
Please Cite: (if you are using this work)
@inproceedings{DBLP:conf/icdm/Rayana16,
author = {Shebuti Rayana and Wen Zhong and Leman Akoglu},
title = {Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective},
booktitle = {Proceeding of IEEE International Conference on Data Mining, {ICDM’16}},
year = {2016},
}
author = {Shebuti Rayana and Wen Zhong and Leman Akoglu},
title = {Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective},
booktitle = {Proceeding of IEEE International Conference on Data Mining, {ICDM’16}},
year = {2016},
}