View in EDS HTML Full Text PDF Full Text

On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles.

Saved in:
Bibliographic Details
Title: On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles.
Authors: Marques, Henrique O.1 (AUTHOR) oli@sdu.dk, Swersky, Lorne2 (AUTHOR), Sander, Jörg2 (AUTHOR), Campello, Ricardo J. G. B.1 (AUTHOR), Zimek, Arthur1 (AUTHOR)
Source: Data Mining & Knowledge Discovery. Jul2023, Vol. 37 Issue 4, p1473-1517. 45p.
Subjects: IEEE Computer Society, Outlier detection, Classification algorithms, Supervised learning, Algorithms, Machine learning, Comparative studies, Classification
Abstract: It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem (Janssens and Postma, in: Proceedings of the 18th annual Belgian-Dutch on machine learning, pp 56–64, 2009; Janssens et al. in: Proceedings of the 2009 ICMLA international conference on machine learning and applications, IEEE Computer Society, pp 147–153, 2009. https://doi.org/10.1109/ICMLA.2009.16). In this paper, we focus on the comparison of one-class classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies in several important aspects. We study a number of one-class classification and unsupervised outlier detection methods in a rigorous experimental setup, comparing them on a large number of datasets with different characteristics, using different performance measures. In contrast to previous comparison studies, where the models (algorithms, parameters) are selected by using examples from both classes (outlier and inlier), here we also study and compare different approaches for model selection in the absence of examples from the outlier class, which is more realistic for practical applications since labeled outliers are rarely available. Our results showed that, overall, SVDD and GMM are top-performers, regardless of whether the ground truth is used for parameter selection or not. However, in specific application scenarios, other methods exhibited better performance. Combining one-class classifiers into ensembles showed better performance than individual methods in terms of accuracy, as long as the ensemble members are properly selected. [ABSTRACT FROM AUTHOR]
Copyright of Data Mining & Knowledge Discovery is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 164754063
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Marques%2C+Henrique+O%2E%22">Marques, Henrique O.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> oli@sdu.dk</i><br /><searchLink fieldCode="AR" term="%22Swersky%2C+Lorne%22">Swersky, Lorne</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sander%2C+Jörg%22">Sander, Jörg</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Campello%2C+Ricardo+J%2E+G%2E+B%2E%22">Campello, Ricardo J. G. B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zimek%2C+Arthur%22">Zimek, Arthur</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Data+Mining+%26+Knowledge+Discovery%22">Data Mining & Knowledge Discovery</searchLink>. Jul2023, Vol. 37 Issue 4, p1473-1517. 45p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22IEEE+Computer+Society%22">IEEE Computer Society</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem (Janssens and Postma, in: Proceedings of the 18th annual Belgian-Dutch on machine learning, pp 56–64, 2009; Janssens et al. in: Proceedings of the 2009 ICMLA international conference on machine learning and applications, IEEE Computer Society, pp 147–153, 2009. https://doi.org/10.1109/ICMLA.2009.16). In this paper, we focus on the comparison of one-class classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies in several important aspects. We study a number of one-class classification and unsupervised outlier detection methods in a rigorous experimental setup, comparing them on a large number of datasets with different characteristics, using different performance measures. In contrast to previous comparison studies, where the models (algorithms, parameters) are selected by using examples from both classes (outlier and inlier), here we also study and compare different approaches for model selection in the absence of examples from the outlier class, which is more realistic for practical applications since labeled outliers are rarely available. Our results showed that, overall, SVDD and GMM are top-performers, regardless of whether the ground truth is used for parameter selection or not. However, in specific application scenarios, other methods exhibited better performance. Combining one-class classifiers into ensembles showed better performance than individual methods in terms of accuracy, as long as the ensemble members are properly selected. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Data Mining & Knowledge Discovery is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=164754063
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10618-023-00931-x
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 45
        StartPage: 1473
    Subjects:
      – SubjectFull: IEEE Computer Society
        Type: general
      – SubjectFull: Outlier detection
        Type: general
      – SubjectFull: Classification algorithms
        Type: general
      – SubjectFull: Supervised learning
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Comparative studies
        Type: general
      – SubjectFull: Classification
        Type: general
    Titles:
      – TitleFull: On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Marques, Henrique O.
      – PersonEntity:
          Name:
            NameFull: Swersky, Lorne
      – PersonEntity:
          Name:
            NameFull: Sander, Jörg
      – PersonEntity:
          Name:
            NameFull: Campello, Ricardo J. G. B.
      – PersonEntity:
          Name:
            NameFull: Zimek, Arthur
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 13845810
          Numbering:
            – Type: volume
              Value: 37
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Data Mining & Knowledge Discovery
              Type: main
ResultId 1