Article

Outlier detection by active learning

Authors:

Bianca Zadrozny,

John LangfordAuthors Info & Claims

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 504 - 509

https://doi.org/10.1145/1150402.1150459

Published: 20 August 2006 Publication History

Abstract

Most existing approaches to outlier detection are based on density estimation methods. There are two notable issues with these methods: one is the lack of explanation for outlier flagging decisions, and the other is the relatively high computational requirement. In this paper, we present a novel approach to outlier detection based on classification, in an attempt to address both of these issues. Our approach isbased on two key ideas. First, we present a simple reduction of outlier detection to classification, via a procedure that involves applying classification to a labeled data set containing artificially generated examples that play the role of potential outliers. Once the task has been reduced to classification, we then invoke a selective sampling mechanism based on active learning to the reduced classification problem. We empirically evaluate the proposed approach using a number of data sets, and find that our method is superior to other methods based on the same reduction to classification, but using standard classification methods. We also show that it is competitive to the state-of-the-art outlier detection methods in the literature based on density estimation, while significantly improving the computational complexity and explanatory power.

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Cited By

Guan YZhang BLi ZZhang D(2025)Enhanced flow number prediction of asphalt mixtures using stacking ensemble-based machine learning model and grey relational analysisConstruction and Building Materials10.1016/j.conbuildmat.2025.140001463(140001)Online publication date: Feb-2025
https://doi.org/10.1016/j.conbuildmat.2025.140001
Sánchez Vinces BSchubert EZimek ACordeiro R(2025)A comparative evaluation of clustering-based outlier detectionData Mining and Knowledge Discovery10.1007/s10618-024-01086-z39:2Online publication date: 1-Mar-2025
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Kechedzhiev ATsvetkova O(2024)Anomaly detection research using Isolation Forest in Machine LearningHerald of Dagestan State Technical University. Technical Sciences10.21822/2073-6185-2024-51-1-106-11251:1(106-112)Online publication date: 16-Apr-2024
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Published In

cover image ACM Conferences

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2006

986 pages

ISBN:1595933395

DOI:10.1145/1150402

Conference Chair:
Tina Eliassi-Rad
LLNL
,
General Chair:
Lyle Ungar
University of Pennsylvania
,
Program Chairs:
Mark Craven
University of Wisconsin
,
Dimitrios Gunopulos
University of California, Riverside

Copyright © 2006 ACM.

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 [email protected]

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Publication History

Published: 20 August 2006

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KDD06

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KDD06: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 20 - 23, 2006

PA, Philadelphia, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.1016/j.conbuildmat.2025.140001
Sánchez Vinces BSchubert EZimek ACordeiro R(2025)A comparative evaluation of clustering-based outlier detectionData Mining and Knowledge Discovery10.1007/s10618-024-01086-z39:2Online publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1007/s10618-024-01086-z
Kechedzhiev ATsvetkova O(2024)Anomaly detection research using Isolation Forest in Machine LearningHerald of Dagestan State Technical University. Technical Sciences10.21822/2073-6185-2024-51-1-106-11251:1(106-112)Online publication date: 16-Apr-2024
https://doi.org/10.21822/2073-6185-2024-51-1-106-112
Cribeiro-Ramallo JArzamasov VBöhm K(2024)Efficient Generation of Hidden Outliers for Improved Outlier DetectionACM Transactions on Knowledge Discovery from Data10.1145/369082718:9(1-21)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3690827
Li NQi YLi CZhao Z(2024)Active Learning for Data Quality Control: A SurveyJournal of Data and Information Quality10.1145/366336916:2(1-45)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3663369
Liu DWang ZLi H(2024)Outlier Detection Using a GPU-Based Parallel Algorithm: Quantum ClusteringInternational Journal on Artificial Intelligence Tools10.1142/S021821302350077X33:04Online publication date: 30-May-2024
https://doi.org/10.1142/S021821302350077X
Kim MYu JKim JOh TChoi J(2024)An Iterative Method for Unsupervised Robust Anomaly Detection Under Data ContaminationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.326702835:10(13327-13339)Online publication date: Oct-2024
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Kiersztyn APylak DHorodelski MKiersztyn KUrbanovich P(2024)Random clustering-based outlier detectorInformation Sciences: an International Journal10.1016/j.ins.2024.120498667:COnline publication date: 1-May-2024
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Hoarau ALemaire VLe Gall YDubois JMartin A(2024)Evidential uncertainty sampling strategies for active learningMachine Language10.1007/s10994-024-06567-2113:9(6453-6474)Online publication date: 1-Sep-2024
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