Skip to main content
SpringerLink
Log in

An Unsupervised Boosting Strategy for Outlier Detection Ensembles

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10937))

Included in the following conference series:

Abstract

Ensemble techniques have been applied to the unsupervised outlier detection problem in some scenarios. Challenges are the generation of diverse ensemble members and the combination of individual results into an ensemble. For the latter challenge, some methods tried to design smaller ensembles out of a wealth of possible ensemble members, to improve the diversity and accuracy of the ensemble (relating to the ensemble selection problem in classification). We propose a boosting strategy for combinations showing improvements on benchmark datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS, vol. 2431, pp. 15–27. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45681-3_2

    Chapter  Google Scholar 

  2. Breunig, M.M., Kriegel, H.-P., Ng, R., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings SIGMOD, pp. 93–104 (2000)

    Google Scholar 

  3. Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: a survey and categorisation. Inf. Fusion 6, 5–20 (2005)

    Article  Google Scholar 

  4. Campos, G.O., Zimek, A., Sander, J., Campello, R.J.G.B., Micenková, B., Schubert, E., Assent, I., Houle, M.E.: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min. Knowl. Disc. 30, 891–927 (2016)

    Article  MathSciNet  Google Scholar 

  5. Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A.: Ensemble selection from libraries of models. In: Proceedings of ICML (2004)

    Google Scholar 

  6. Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45014-9_1

    Chapter  Google Scholar 

  7. Gao, J., Tan, P.-N.: Converting output scores from outlier detection algorithms into probability estimates. In: Proceedings of ICDM, pp. 212–221 (2006)

    Google Scholar 

  8. Ghosh, J., Acharya, A.: Cluster ensembles. WIREs DMKD 1(4), 305–315 (2011)

    Google Scholar 

  9. Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM TKDD 1(1) (2007)

    Article  Google Scholar 

  10. Hautamäki, V., Kärkkäinen, I., Fränti, P.: Outlier detection using k-nearest neighbor graph. In: Proceedings of ICPR, pp. 430–433 (2004)

    Google Scholar 

  11. Iam-On, N., Boongoen, T.: Comparative study of matrix refinement approaches for ensemble clustering. Mach. Learn. (2013)

    Google Scholar 

  12. Jin, W., Tung, A.K.H., Han, J., Wang, W.: Ranking outliers using symmetric neighborhood relationship. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 577–593. Springer, Heidelberg (2006). https://doi.org/10.1007/11731139_68

    Chapter  Google Scholar 

  13. Kirner, E., Schubert, E., Zimek, A.: Good and bad neighborhood approximations for outlier detection ensembles. In: Beecks, C., Borutta, F., Kröger, P., Seidl, T. (eds.) SISAP 2017. LNCS, vol. 10609, pp. 173–187. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68474-1_12

    Chapter  Google Scholar 

  14. Kriegel, H.-P., Kröger, P., Schubert, E., Zimek, A.: LoOP: local outlier probabilities. In: Proceedings of CIKM, pp. 1649–1652 (2009)

    Google Scholar 

  15. Kriegel, H.-P., Kröger, P., Schubert, E., Zimek, A.: Interpreting and unifying outlier scores. In: Proceedings of SDM, pp. 13–24 (2011)

    Chapter  Google Scholar 

  16. Kriegel, H.-P., Schubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data. In: Proceedings of KDD, pp. 444–452 (2008)

    Google Scholar 

  17. Latecki, L.J., Lazarevic, A., Pokrajac, D.: Outlier detection with kernel density functions. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 61–75. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73499-4_6

    Chapter  Google Scholar 

  18. Lazarevic, A., Kumar, V.: Feature bagging for outlier detection. In: Proceedings of KDD, pp. 157–166 (2005)

    Google Scholar 

  19. Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation-based anomaly detection. ACM TKDD 6(1), 3:1–3:39 (2012)

    Google Scholar 

  20. Margineantu, D.D., Dietterich, T.G.: Pruning adaptive boosting. In: Proceedings of ICML, pp. 211–218 (1997)

    Google Scholar 

  21. Nguyen, H.V., Ang, H.H., Gopalkrishnan, V.: Mining outliers with ensemble of heterogeneous detectors on random subspaces. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010, Part I. LNCS, vol. 5981, pp. 368–383. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12026-8_29

    Chapter  Google Scholar 

  22. Nguyen, N., Caruana, R.: Consensus clusterings. In: Proceedings of ICDM, pp. 607–612 (2007)

    Google Scholar 

  23. Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: Proceedings of SIGMOD, pp. 427–438 (2000)

    Article  Google Scholar 

  24. Rayana, S., Akoglu, L.: Less is more: building selective anomaly ensembles. ACM TKDD 10(4), 42:1–42:33 (2016)

    Google Scholar 

  25. Rayana, S., Zhong, W., Akoglu, L.: Sequential ensemble learning for outlier detection: a bias-variance perspective. In: Proceedings of ICDM, pp. 1167–1172 (2016)

    Google Scholar 

  26. Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33, 1–39 (2010)

    Article  Google Scholar 

  27. Salehi, M., Zhang, X., Bezdek, J.C., Leckie, C.: Smart sampling: a novel unsupervised boosting approach for outlier detection. In: Kang, B.H., Bai, Q. (eds.) AI 2016. LNCS (LNAI), vol. 9992, pp. 469–481. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50127-7_40

    Chapter  Google Scholar 

  28. Schapire, R.E., Freund, Y.: Boosting. Foundations and Algorithms. MIT Press, Cambridge (2012)

    MATH  Google Scholar 

  29. Schubert, E., Wojdanowski, R., Zimek, A., Kriegel, H.-P.: On evaluation of outlier rankings and outlier scores. In: Proceedings of SDM, pp. 1047–1058 (2012)

    Chapter  Google Scholar 

  30. Schubert, E., Zimek, A., Kriegel, H.-P.: Generalized outlier detection with flexible kernel density estimates. In: Proceedings of SDM, pp. 542–550 (2014)

    Chapter  Google Scholar 

  31. Schubert, E., Zimek, A., Kriegel, H.-P.: Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min. Knowl. Disc. 28(1), 190–237 (2014)

    Article  MathSciNet  Google Scholar 

  32. Strehl, A., Ghosh, J.: Cluster ensembles – a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2002)

    MathSciNet  MATH  Google Scholar 

  33. Tang, J., Chen, Z., Fu, A.W., Cheung, D.W.: Enhancing effectiveness of outlier detections for low density patterns. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 535–548. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47887-6_53

    Chapter  Google Scholar 

  34. Topchy, A., Jain, A., Punch, W.: Clustering ensembles: models of concensus and weak partitions. IEEE TPAMI 27(12), 1866–1881 (2005)

    Article  Google Scholar 

  35. Tsoumakas, G., Partalas, I., Vlahavas, I.: An ensemble pruning primer. In: Okun, O., Valentini, G. (eds.) Applications of Supervised and Unsupervised Ensemble Methods. SCI, vol. 245, pp. 1–13. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03999-7_1

    Chapter  Google Scholar 

  36. Valentini, G., Masulli, F.: Ensembles of learning machines. In: Marinaro, M., Tagliaferri, R. (eds.) WIRN 2002. LNCS, vol. 2486, pp. 3–20. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45808-5_1

    Chapter  MATH  Google Scholar 

  37. Zhang, K., Hutter, M., Jin, H.: A new local distance-based outlier detection approach for scattered real-world data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 813–822. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01307-2_84

    Chapter  Google Scholar 

  38. Zhou, Z., Wu, J., Tang, W.: Ensembling neural networks: many could be better than all. Artif. Intell. 137(1–2), 239–263 (2002)

    Article  MathSciNet  Google Scholar 

  39. Zhou, Z.-H.: Ensemble Methods. Foundations and Algorithms. CRC Press, Boca Raton (2012)

    Google Scholar 

  40. Zimek, A., Campello, R.J.G.B., Sander, J.: Ensembles for unsupervised outlier detection: challenges and research questions. SIGKDD Explor. 15(1), 11–22 (2013)

    Article  Google Scholar 

  41. Zimek, A., Campello, R.J.G.B., Sander, J.: Data perturbation for outlier detection ensembles. In: Proceedings of SSDBM, pp. 13:1–13:12 (2014)

    Google Scholar 

  42. Zimek, A., Gaudet, M., Campello, R.J.G.B., Sander, J.: Subsampling for efficient and effective unsupervised outlier detection ensembles. In: Proceedings of KDD, pp. 428–436 (2013)

    Google Scholar 

Download references

Acknowledgments

This work was partially supported by CAPES - Brazil, Fapemig, CNPq, and by projects InWeb, MASWeb, EUBra-BIGSEA (H2020-EU.2.1.1 690116, Brazil/MCTI/RNP GA-000650/04), INCT-Cyber, and Atmosphere (H2020-EU 777154, Brazil/MCTI/RNP 51119).

Author information

Authors and Affiliations

  1. Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, Brazil

    Guilherme O. Campos & Wagner Meira Jr.

  2. Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark

    Guilherme O. Campos & Arthur Zimek

Authors
  1. Guilherme O. Campos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arthur Zimek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wagner Meira Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guilherme O. Campos .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Dinh Phung

  2. National Chiao Tung University, Hsinchu City, Taiwan

    Vincent S. Tseng

  3. Monash University, Clayton, Victoria, Australia

    Geoffrey I. Webb

  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Bao Ho

  5. University of Melbourne, Melbourne, Victoria, Australia

    Mohadeseh Ganji

  6. University of Melbourne, Melbourne, Victoria, Australia

    Lida Rashidi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Campos, G.O., Zimek, A., Meira, W. (2018). An Unsupervised Boosting Strategy for Outlier Detection Ensembles. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93034-3_45

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93033-6

  • Online ISBN: 978-3-319-93034-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation