Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128808
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Type: Journal article
Title: Heterogeneous univariate outlier ensembles in multidimensional data
Author: Pang, G.
Cao, L.
Citation: ACM Transactions on Knowledge Discovery from Data, 2020; 14(6):68-1-68-27
Publisher: Association for Computing Machinery
Issue Date: 2020
ISSN: 1556-4681
1556-472X
Statement of
Responsibility: 
Guansong Pang, Longbing Cao
Abstract: In outlier detection, recent major research has shifted from developing univariate methods to multivariate methods due to the rapid growth of multidimensional data. However, one typical issue of this paradigm shift is that many multidimensional data often mainly contains univariate outliers, in which many features are actually irrelevant. In such cases, multivariate methods are ineffective in identifying such outliers due to the potential biases and the curse of dimensionality brought by irrelevant features. Those univariate outliers might be well detected by applying univariate outlier detectors in individually relevant features. However, it is very challenging to choose a right univariate detector for each individual feature since different features may take very different probability distributions. To address this challenge, we introduce a novel Heterogeneous Univariate Outlier Ensembles (HUOE) framework and its instance ZDD to synthesize a set of heterogeneous univariate outlier detectors as base learners to build heterogeneous ensembles that are optimized for each individual feature. Extensive results on 19 real-world datasets and a collection of synthetic datasets show that ZDD obtains 5%–14% average AUC improvement over four state-of-the-art multivariate ensembles and performs substantially more robustly w.r.t. irrelevant features.
Keywords: Outlier detection; outlier ensemble; anomaly detection; univariate outlier; multidimensional data; heterogeneous data
Rights: © 2020 Association for Computing Machinery. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
RMID: 1000027467
DOI: 10.1145/3403934
Grant ID: http://purl.org/au-research/grants/arc/DP190101079
Appears in Collections:Australian Institute for Machine Learning publications

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