Please use this identifier to cite or link to this item: http://archive.cmb.ac.lk:8080/xmlui/handle/70130/7462
Title: Violation-based Feature Selection for Isolation Forest
Authors: Dissanayake, D.M.
Navarathna, R.
Viswakula, S.
Keywords: Isolation Forest
Anomaly Detection
Rule Violation
Issue Date: 2024
Publisher: University of Colombo
Citation: Dissanayake, D.M., Navarathna, R., and Viswakula, S. (2024). Violation-based Feature Selection for Isolation Forest. Proceedings: University of Colombo Annual Research Symposium 2024, p.298.
Abstract: Anomaly detection is crucial in sectors like finance and healthcare to identify deviations from normal behavior. The Isolation Forest (ISF) algorithm, introduced by Liu et al. (2008), is effective but has limitations, such as bias towards correlated variables and suboptimal results with irrelevant features. This study introduces the Violation Features Based Isolation Forest (VFIF) algorithm, ...
URI: http://archive.cmb.ac.lk:8080/xmlui/handle/70130/7462
Appears in Collections:Department of Statistics

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