Commentary: a decomposition of the outlier detection problem into a set of supervised learning problems

Ye Zhu, Kai Ming Ting

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    This article discusses the material in relation to iForest (Liu et al. in ACM Trans Knowl Discov Data 6(1):3, 2012) reported in a recent Machine Learning Journal paper by Paulheim and Meusel (Mach Learn 100(2–3):509–531, 2015). It presents an empirical comparison result of iForest using the default parameter settings suggested by its creator (Liu et al. 2012) and iForest using the settings employed by Paulheim and Meusel (2015). This comparison has an impact on the conclusion made by Paulheim and Meusel (2015).

    Original languageEnglish
    Pages (from-to)301-304
    Number of pages4
    JournalMachine Learning
    Volume105
    Issue number2
    DOIs
    Publication statusPublished - 1 Nov 2016

    Keywords

    • Anomaly detection
    • Isolation forest
    • Outlier detection

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