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Model Combination Methods for Outlier Ensembles

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Outlier Ensembles

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Abstract

An important part of the process of creating outlier ensembles is to combine the outputs of different detectors. The precise method for model combination has a significant impact on the effectiveness of a particular outlier detection method because of the varying theoretical effects of different combination methods. For example, the impact of the scheme of averaging is quite different from that of maximization in terms of the bias and variance of the result. Therefore, the choice of model combination has a crucial effect on the results of the ensemble.

Only government can take perfectly good paper, cover it with perfectly good ink and make the combination worthless.

Milton Friedman

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Notes

  1. 1.

    The original LOF paper recognized the problem of dilution from irrelevant ensemble components and therefore suggested the use of the maximization function.

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Authors and Affiliations

  1. IBM T. J. Watson Research Center, Yorktown Heights, NY, USA

    Charu C. Aggarwal & Saket Sathe

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  1. Charu C. Aggarwal
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  2. Saket Sathe
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Correspondence to Charu C. Aggarwal .

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Aggarwal, C.C., Sathe, S. (2017). Model Combination Methods for Outlier Ensembles. In: Outlier Ensembles. Springer, Cham. https://doi.org/10.1007/978-3-319-54765-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-54765-7_5

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  • Online ISBN: 978-3-319-54765-7

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