An Unsupervised Boosting Strategy for Outlier Detection Ensembles

Guilherme Oliveira Campos, Arthur Zimek, Wagner Meira Jr.

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Resumé

Ensemble techniques have been applied to the unsupervised outlier detection problem in some scenarios. Challenges are the generation of diverse ensemble members and the combination of individual results into an ensemble. For the latter challenge, some methods tried to design smaller ensembles out of a wealth of possible ensemble members, to improve the diversity and accuracy of the ensemble (relating to the ensemble selection problem in classification). We propose a boosting strategy for combinations showing improvements on benchmark datasets.
OriginalsprogEngelsk
TitelAdvances in Knowledge Discovery and Data Mining : Proceedings, Part I
RedaktørerDinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi
Vol/bind10937
ForlagSpringer
Publikationsdato2018
Sider564-576
ISBN (Trykt)978-3-319-93033-6
ISBN (Elektronisk)978-3-319-93034-3
DOI
StatusUdgivet - 2018
BegivenhedPacific-Asia Conference on Knowledge Discovery and Data Mining - Melbourne, Australien
Varighed: 3. jun. 20186. jun. 2018
Konferencens nummer: 22
http://prada-research.net/pakdd18/

Konference

KonferencePacific-Asia Conference on Knowledge Discovery and Data Mining
Nummer22
LandAustralien
ByMelbourne
Periode03/06/201806/06/2018
Internetadresse
NavnLecture Notes in Computer Science
Vol/bind10937
ISSN0302-9743
NavnLecture Notes in Artificial Intelligence
Vol/bind10937

Fingeraftryk

outlier
detection
method

Citer dette

Campos, G. O., Zimek, A., & Meira Jr., W. (2018). An Unsupervised Boosting Strategy for Outlier Detection Ensembles. I D. Phung, V. S. Tseng, G. I. Webb, B. Ho, M. Ganji, & L. Rashidi (red.), Advances in Knowledge Discovery and Data Mining: Proceedings, Part I (Bind 10937, s. 564-576). Springer. Lecture Notes in Computer Science, Bind. 10937, Lecture Notes in Artificial Intelligence, Bind. 10937 https://doi.org/10.1007/978-3-319-93034-3_45
Campos, Guilherme Oliveira ; Zimek, Arthur ; Meira Jr., Wagner. / An Unsupervised Boosting Strategy for Outlier Detection Ensembles. Advances in Knowledge Discovery and Data Mining: Proceedings, Part I. red. / Dinh Phung ; Vincent S. Tseng ; Geoffrey I. Webb ; Bao Ho ; Mohadeseh Ganji ; Lida Rashidi. Bind 10937 Springer, 2018. s. 564-576 (Lecture Notes in Computer Science, Bind 10937). (Lecture Notes in Artificial Intelligence, Bind 10937).
@inproceedings{b22523e21397450fb1cf37c6f60ee072,
title = "An Unsupervised Boosting Strategy for Outlier Detection Ensembles",
abstract = "Ensemble techniques have been applied to the unsupervised outlier detection problem in some scenarios. Challenges are the generation of diverse ensemble members and the combination of individual results into an ensemble. For the latter challenge, some methods tried to design smaller ensembles out of a wealth of possible ensemble members, to improve the diversity and accuracy of the ensemble (relating to the ensemble selection problem in classification). We propose a boosting strategy for combinations showing improvements on benchmark datasets.",
keywords = "Outlier detection, Ensembles, Boosting, Ensemble selection",
author = "Campos, {Guilherme Oliveira} and Arthur Zimek and {Meira Jr.}, Wagner",
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Campos, GO, Zimek, A & Meira Jr., W 2018, An Unsupervised Boosting Strategy for Outlier Detection Ensembles. i D Phung, V S. Tseng, G I. Webb, B Ho, M Ganji & L Rashidi (red), Advances in Knowledge Discovery and Data Mining: Proceedings, Part I. bind 10937, Springer, Lecture Notes in Computer Science, bind 10937, Lecture Notes in Artificial Intelligence, bind 10937, s. 564-576, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Melbourne, Australien, 03/06/2018. https://doi.org/10.1007/978-3-319-93034-3_45

An Unsupervised Boosting Strategy for Outlier Detection Ensembles. / Campos, Guilherme Oliveira; Zimek, Arthur; Meira Jr., Wagner.

Advances in Knowledge Discovery and Data Mining: Proceedings, Part I. red. / Dinh Phung; Vincent S. Tseng; Geoffrey I. Webb; Bao Ho; Mohadeseh Ganji; Lida Rashidi. Bind 10937 Springer, 2018. s. 564-576 (Lecture Notes in Computer Science, Bind 10937). (Lecture Notes in Artificial Intelligence, Bind 10937).

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

TY - GEN

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AU - Campos, Guilherme Oliveira

AU - Zimek, Arthur

AU - Meira Jr., Wagner

PY - 2018

Y1 - 2018

N2 - Ensemble techniques have been applied to the unsupervised outlier detection problem in some scenarios. Challenges are the generation of diverse ensemble members and the combination of individual results into an ensemble. For the latter challenge, some methods tried to design smaller ensembles out of a wealth of possible ensemble members, to improve the diversity and accuracy of the ensemble (relating to the ensemble selection problem in classification). We propose a boosting strategy for combinations showing improvements on benchmark datasets.

AB - Ensemble techniques have been applied to the unsupervised outlier detection problem in some scenarios. Challenges are the generation of diverse ensemble members and the combination of individual results into an ensemble. For the latter challenge, some methods tried to design smaller ensembles out of a wealth of possible ensemble members, to improve the diversity and accuracy of the ensemble (relating to the ensemble selection problem in classification). We propose a boosting strategy for combinations showing improvements on benchmark datasets.

KW - Outlier detection

KW - Ensembles

KW - Boosting

KW - Ensemble selection

U2 - 10.1007/978-3-319-93034-3_45

DO - 10.1007/978-3-319-93034-3_45

M3 - Article in proceedings

SN - 978-3-319-93033-6

VL - 10937

T3 - Lecture Notes in Computer Science

SP - 564

EP - 576

BT - Advances in Knowledge Discovery and Data Mining

A2 - Phung, Dinh

A2 - S. Tseng, Vincent

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Campos GO, Zimek A, Meira Jr. W. An Unsupervised Boosting Strategy for Outlier Detection Ensembles. I Phung D, S. Tseng V, I. Webb G, Ho B, Ganji M, Rashidi L, red., Advances in Knowledge Discovery and Data Mining: Proceedings, Part I. Bind 10937. Springer. 2018. s. 564-576. (Lecture Notes in Computer Science, Bind 10937). (Lecture Notes in Artificial Intelligence, Bind 10937). https://doi.org/10.1007/978-3-319-93034-3_45