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Improving iForest with relative mass

conference contribution
posted on 2014-01-01, 00:00 authored by Sunil AryalSunil Aryal, Kai Ming Ting, Jonathan R Wells, Takashi Washio
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.

History

Volume

8444

Pagination

510-521

Location

Tainan, Taiwan

Start date

2014-05-13

End date

2014-05-16

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

978-3-319-06605-9

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2014, Springer International Publishing Switzerland

Editor/Contributor(s)

Tseng VS, Ho TB, Zhou Z-H, Chen ALP, Kao H-Y

Title of proceedings

PAKDD 2014 : Proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining

Event

Knowledge Discovery and Data Mining. Conference (18th : 2014 : Tainan, Taiwan)

Issue

PART 2

Publisher

Springer International Publishing

Place of publication

Cham, Switzerland

Series

Knowledge Discovery and Data Mining Conference

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