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Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Barbado Gonzalez, AlbertoAuthorBarbado ACorresponding Author

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November 18, 2021
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Rule extraction in unsupervised anomaly detection for model explainability: Application to OneClass SVM[Formula presented]

Publicated to:Expert Systems With Applications. 189 116100-116100 - 2022-01-01 189(), DOI: 10.1016/j.eswa.2021.116100

Authors: Barbado, Alberto; Corcho, Oscar; Benjamins, Richard

Affiliations

Telefon IoT & Big Data Tech SA, Madrid, Spain - Author
Telefónica - Author
Univ Politecn Madrid, Dept Inteligencia Artificial, Madrid 28223, Spain - Author
Universidad Politécnica de Madrid - Author
Universidad Politécnica de Madrid , Telefonica - Author
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Abstract

OneClass SVM is a popular method for unsupervised anomaly detection. As many other methods, it suffers from the black box problem: it is difficult to justify, in an intuitive and simple manner, why the decision frontier is identifying data points as anomalous or non anomalous. This problem is being widely addressed for supervised models. However, it is still an uncharted area for unsupervised learning. In this paper, we evaluate several rule extraction techniques over OneClass SVM models, while presenting alternative designs for some of those algorithms. Furthermore, we propose algorithms for computing metrics related to eXplainable Artificial Intelligence (XAI) regarding the “comprehensibility”, “representativeness”, “stability” and “diversity” of the extracted rules. We evaluate our proposals with different data sets, including real-world data coming from industry. Consequently, our proposal contributes to extending XAI techniques to unsupervised machine learning models.

Keywords

anomaly detectionmetricsoneclass svmrule extractionunsupervised learningAnomaly detectionIndustry, innovation and infrastructureMetricsOneclass svmRule extractionSupport vector machinesUnsupervised learningXai

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Expert Systems With Applications due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position 6/86, thus managing to position itself as a Q1 (Primer Cuartil), in the category Operations Research & Management Science. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 2.22. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.39 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 20.52 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-16, the following number of citations:

  • WoS: 29
  • Scopus: 39
  • Open Alex: 39

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-16:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 74.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 86 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 4.25.
  • The number of mentions on the social network X (formerly Twitter): 10 (Altmetric).
Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 9 - Industry, innovation and infrastructure, with a probability of 60% according to the mBERT algorithm developed by Aurora University.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (CORCHO GARCIA, OSCAR) and Last Author (CORCHO GARCIA, OSCAR).

the authors responsible for correspondence tasks have been CORCHO GARCIA, OSCAR and Corcho, Oscar.