Outlier detection with one-class classifiers from ML and KDD

J.H.M. Janssens, I. Flesch, E.O. Postma

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    24 Citations (Scopus)

    Abstract

    Experimental results show that LOF and SVDD are the two best-performing methods. It is concluded that both fields offer outlier-detection methods that are competitive in performance and that bridging the gap between both fields may facilitate the development of outlier-detection methods.
    Original languageEnglish
    Title of host publicationProceedings of the Eight International Conference on Machine Learning and Applications
    EditorsA. Wani, M. Kantardzic, V. Palade, L. Kurgan, Y. Qi
    Place of PublicationMiami, FL, USA
    PublisherICMLA
    Pages147-153
    Publication statusPublished - 2009

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  • Cite this

    Janssens, J. H. M., Flesch, I., & Postma, E. O. (2009). Outlier detection with one-class classifiers from ML and KDD. In A. Wani, M. Kantardzic, V. Palade, L. Kurgan, & Y. Qi (Eds.), Proceedings of the Eight International Conference on Machine Learning and Applications (pp. 147-153). ICMLA. http://www.icmla-conference.org/icmla09/