Sequential Ensemble Learning for Outlier Detection

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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)

                                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},




arXiv preprint