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

Title: Commentary : A decomposition of the outlier detection problem into a set of supervised learning problems
Creator: Zhu, Ye; Ting, Kaiming
Date: 2016
Type: Text; Journal article
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/162080
Identifier: vital:12634
Identifier: https://doi.org/10.1007/s10994-016-5566-8
Identifier: ISBN:0885-6125
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). © 2016, The Author(s).
Publisher: Springer New York LLC
Relation: Machine Learning Vol. 105, no. 2 (2016), p. 301-304
Rights: This metadata is freely available under a CCO license
Subject: 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; 1702 Cognitive Science; Anomaly detection; Isolation forest; Outlier detection; Artificial intelligence; Data handling; Learning systems; Default parameters; Empirical - comparisons; Supervised learning problems; Statistics
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