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Author: search.filters.author.González-Zelaya, VladimiroHas files: No

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    Item type:Publication,
    Transformación digital en ciencias administrativas y contabilidad: tendencias de investigación en Scopus
    (Pro-Metrics, 2024)
    Salgado-García, Jorge Arturo
    ;
    Terán-Bustamante, Antonia  
    ;
    González-Zelaya, Vladimiro
    Objective. Identify thematic trends in digital transformation in administrative sciences and accounting. Design/Methodology/Approach. A bibliometric analysis was performed considering 7,519 documents indexed in the Scopus database between 1970 and 2023. The analysis was performed using the authors' keywords to identify thematic trends. Results/Discussion. Thematic cores related to Covid-19, digital marketing, emerging technologies, innovation, industry 4.0, and Fintech were identified. Conclusions. Covid-19 promoted digital transformation and research in this field applied to administrative sciences and accounting. However, the advancement of digital technologies has influenced scientific production. Likewise, other trends, such as sustainability, converged in the generation of knowledge. © Iberoamerican Journal of Science Measurement and Communication.
    Scopus© Citations 6  42
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    Item type:Publication,
    Preprocessing Matters: Automated Pipeline Selection for Fair Classification
    (2023)
    González-Zelaya, Vladimiro
    ;
    Salas, Julián
    ;
    Prangle, Dennis
    ;
    Missier, Paolo
    Improving fairness by manipulating the preprocessing stages of classification pipelines is an active area of research, closely related to AutoML. We propose a genetic optimisation algorithm, FairPipes, which optimises for user-defined combinations of fairness and accuracy and for multiple definitions of fairness, providing flexibility in the fairness-accuracy trade-off. FairPipes heuristically searches through a large space of pipeline configurations, achieving near-optimality efficiently, presenting the user with an estimate of the solutions’ Pareto front. We also observe that the optimal pipelines differ for different datasets, suggesting that no “universal best” pipeline exists and confirming that FairPipes fills a niche in the fairness-aware AutoML space.
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