Improving iForest with Relative Mass

  • Sunil Aryal
  • Kai Ming Ting
  • Jonathan R. Wells
  • Takashi Washio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8444)


iForest uses a collection of isolation trees to detect anomalies. While it is effective in detecting global anomalies, it fails to detect local anomalies in data sets having multiple clusters of normal instances because the local anomalies are masked by normal clusters of similar density and they become less susceptible to isolation. In this paper, we propose a very simple but effective solution to overcome this limitation by replacing the global ranking measure based on path length with a local ranking measure based on relative mass that takes local data distribution into consideration. We demonstrate the utility of relative mass by improving the task specific performance of iForest in anomaly detection and information retrieval tasks.


Relative mass iForest ReFeat anomaly detection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: Proceedings of the Eighth IEEE International Conference on Data Mining, pp. 413–422 (2008)Google Scholar
  2. 2.
    Ting, K.M., Zhou, G.T., Liu, F.T., Tan, S.C.: Mass estimation. Machine Learning 90(1), 127–160 (2013)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Zhou, G.T., Ting, K.M., Liu, F.T., Yin, Y.: Relevance feature mapping for content-based multimedia information retrieval. Pattern Recognition 45(4), 1707–1720 (2012)CrossRefGoogle Scholar
  4. 4.
    Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: Identifying Density-Based Local Outliers. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 93–104 (2000)Google Scholar
  5. 5.
    Ting, K., Washio, T., Wells, J., Liu, F., Aryal, S.: DEMass: a new density estimator for big data. Knowledge and Information Systems 35(3), 493–524 (2013)CrossRefGoogle Scholar
  6. 6.
    Rui, Y., Huang, T., Ortega, M., Mehrotra, S.: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology 8(5), 644–655 (1998)CrossRefGoogle Scholar
  7. 7.
    He, J., Li, M., Zhang, H.J., Tong, H., Zhang, C.: Manifold-ranking based image retrieval. In: Proceedings of the 12th Annual ACM International Conference on Multimedia, pp. 9–16. ACM, New York (2004)Google Scholar
  8. 8.
    Giacinto, G., Roli, F.: Instance-based relevance feedback for image retrieval. In: Advances in Neural Information Processing Systems, vol. 17, pp. 489–496 (2005)Google Scholar
  9. 9.
    Zhou, Z.H., Dai, H.B.: Query-sensitive similarity measure for content-based image retrieval. In: Proceedings of the Sixth International Conference on Data Mining, pp. 1211–1215 (2006)Google Scholar
  10. 10.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)Google Scholar
  11. 11.
    Achtert, E., Hettab, A., Kriegel, H.-P., Schubert, E., Zimek, A.: Spatial outlier detection: Data, algorithms, visualizations. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 512–516. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 10(5), 293–302 (2002)CrossRefGoogle Scholar
  13. 13.
    Zhou, Z.H., Chen, K.J., Dai, H.B.: Enhancing relevance feedback in image retrieval using unlabeled data. ACM Transactions on Information Systems 24(2), 219–244 (2006)CrossRefGoogle Scholar
  14. 14.
    Ting, K.M., Fernando, T.L., Webb, G.I.: Mass-based Similarity Measure: An Effective Alternative to Distance-based Similarity Measures. Technical Report 2013/276, Calyton School of IT, Monash University, Australia (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sunil Aryal
    • 1
  • Kai Ming Ting
    • 2
  • Jonathan R. Wells
    • 1
  • Takashi Washio
    • 3
  1. 1.Monash UniversityAustralia
  2. 2.Federation UniversityAustralia
  3. 3.Osaka UniversityOsakaJapan

Personalised recommendations