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Analysis of institutional authors

Fabra-Boluda, RCorresponding AuthorFerri, CAuthorMartínez-Plumed, FAuthorHernández-Orallo, JAuthorRamirez-Quintan, MjAuthor

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October 30, 2024
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Family and Prejudice: A Behavioural Taxonomy of Machine Learning Techniques

Publicated to:Frontiers In Artificial Intelligence And Applications. 325 1135-1142 - 2020-01-01 325(), DOI: 10.3233/FAIA200211

Authors: Fabra-Boluda, Raul; Ferri, Cesar; Martinez-Plumed, Fernando; Hernandez-Orallo, Jose; Ramirez-Quintan, M Jose

Affiliations

European Commiss, JRC - Author
Univ Politecn Valencia, Valencian Res Inst Artificial Intelligence VRAIN - Author

Abstract

One classical way of characterising the rich range of machine learning techniques is by defining 'families', according to their formulation and learning strategy (e.g., neural networks, Bayesian methods, etc.). However, this taxonomy of learning techniques does not consider the extent to which models built with techniques from the same or different family agree on their outputs, especially when their predictions have to extrapolate in sparse zones where insufficient training data was available. In this paper we present a new taxonomy of machine learning techniques for classification, where families are clustered according to their degree of (dis)agreement in behaviour considering both dense and sparse zones, using Cohen's kappa statistic. To this end, we use a representative collection of datasets and learning techniques. We finally validate the taxonomy by performing a number of experiments for technique selection. We show that ranking techniques by only following prejudice -the reputation they have for other problems- is worse than selecting techniques based on family diversity.

Keywords

Bayesian methodsBayesian networksClassifiersCohen's kappasItem response theoryKappa statisticLearning algorithmsLearning strategyLearning techniquesMachine learningMachine learning techniquesNeural-networksRanking techniqueTaxonomiesTechnique selectionTraining data

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Frontiers In Artificial Intelligence And Applications, Q4 Agency Scopus (SJR), its regional focus and specialization in Artificial Intelligence, give it significant recognition in a specific niche of scientific knowledge at an international level.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-17:

  • WoS: 1
  • Scopus: 4

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 2025-07-17:

  • 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: 3 (PlumX).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Belgium.

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 (DeGiacomo, G) and Last Author (Barro, S).

the author responsible for correspondence tasks has been Fabra Boluda, Raúl.