Model Combination Methods for Outlier Ensembles

  • Charu C. AggarwalEmail author
  • Saket Sathe


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.


Receiver Operating Characteristic Curve Maximization Function Combination Method Variance Reduction Model Combination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    C. C. Aggarwal. Outlier Ensembles: Position Paper, ACM SIGKDD Explorations, 14(2), pp. 49–58, December, 2012.Google Scholar
  2. 2.
    C. C. Aggarwal. Recommender Systems: The Textbook, Springer, 2016.Google Scholar
  3. 3.
    C. C. Aggarwal. Outlier Analysis, Second Edition, Springer, 2017.Google Scholar
  4. 4.
    C. C. Aggarwal and S. Sathe. Theoretical Foundations and Algorithms for Outlier Ensembles, ACM SIGKDD Explorations, 17(1), June 2015.Google Scholar
  5. 5.
    C. C. Aggarwal and P. S. Yu. Outlier Detection in High Dimensional Data, ACM SIGMOD Conference, 2001.Google Scholar
  6. 6.
    C. C. Aggarwal and P. S. Yu. Outlier Detection in Graph Streams, IEEE ICDE Conference, 2011.Google Scholar
  7. 7.
    D. Barbara, Y. Li, J. Couto, J.-L. Lin, and S. Jajodia. Bootstrapping a Data Mining Intrusion Detection System. Symposium on Applied Computing, 2003.Google Scholar
  8. 8.
    M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander. LOF: Identifying Density-based Local Outliers, ACM SIGMOD Conference, 2000.Google Scholar
  9. 9.
    L. Brieman. Bagging Predictors. Machine Learning, 24(2), pp. 123–140, 1996.Google Scholar
  10. 10.
    L. Brieman. Random Forests. Journal Machine Learning archive, 45(1), pp. 5–32, 2001.Google Scholar
  11. 11.
    G. Brown, J. Wyatt, R. Harris, and X. Yao. Diversity creation methods: a survey and categorisation. Information Fusion, 6:5(20), 2005.Google Scholar
  12. 12.
    P. Buhlmann. Bagging, subagging and bragging for improving some prediction algorithms, Recent advances and trends in nonparametric statistics, Elsevier, 2003.Google Scholar
  13. 13.
    P. Buhlmann, B. Yu. Analyzing bagging. Annals of Statistics, pp. 927–961, 2002.Google Scholar
  14. 14.
    A. Buja, W. Stuetzle. Observations on bagging. Statistica Sinica, 16(2), 323, 2006.Google Scholar
  15. 15.
    J. Chen, S. Sathe, C. Aggarwal and D. Turaga. Outlier detection with ensembles of autoencoders. In preparation, 2017.Google Scholar
  16. 16.
    J. Gao, P.-N. Tan. Converting output scores from outlier detection algorithms into probability estimates. ICDM Conference, 2006.Google Scholar
  17. 17.
    Z. He, S. Deng and X. Xu. A Unified Subspace Outlier Ensemble Framework for Outlier Detection, Advances in Web Age Information Management, 2005.Google Scholar
  18. 18.
    M. Kendall. A New Measure of Rank Correlation. Biometrika, 30(1/2), 81–93, 1938.Google Scholar
  19. 19.
    A. Lazarevic, and V. Kumar. Feature Bagging for Outlier Detection, ACM KDD Conference, 2005.Google Scholar
  20. 20.
    B. Micenkova, B. McWiliams, and I. Assent. Learning Outlier Ensembles: The Best of Both Worlds – Supervised and Unsupervised. Outlier Detection and Description Workshop, 2014. Extended version:
  21. 21.
    S. Papadimitriou, H. Kitagawa, P. Gibbons, and C. Faloutsos, LOCI: Fast outlier detection using the local correlation integral, ICDE Conference, 2003.Google Scholar
  22. 22.
    M. Shyu, S. Chen, K. Sarinnapakorn, L. Chang. A novel anomaly detection scheme based on principal component classifier. ICDMW, 2003.Google Scholar
  23. 23.
    D. Wolpert. Stacked Generalization, Neural Networks, 5(2), pp. 241–259, 1992.Google Scholar
  24. 24.
    A. Zimek, R. Campello, J. Sander. Ensembles for unsupervised outlier detection: Challenges and research questions, SIGKDD Explorations, 15(1), 2013.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.IBM T. J. Watson Research CenterYorktown HeightsUSA

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