Burusco Juandeaburre, Ana

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https://academica-e.unavarra.es/handle/2454/48763

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Burusco Juandeaburre

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Ana

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Estadística, Informática y Matemáticas

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ISC. Institute of Smart Cities

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0000-0003-4974-9488

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88335

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49

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2018 - 20192
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End date: 2019 × Subject: Choquet integral ×

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  • Thumbnail Image
    PublicationOpen Access
    Multivalued contexts associated with criteria
    (Taylor & Francis, 2018) Alcalde, Cristina; Burusco Juandeaburre, Ana; Automatika eta Konputazioa; Institute of Smart Cities - ISC; Automática y Computación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    We are interested in the study of L-fuzzy contexts taking into account different criteria. These contexts arise when we want to analyze the relationship between objects and attributes from different points of view. Furthermore, in some occasions, these L-fuzzy contexts have several values for every pair object attribute. We will see how both the WOWA operators and the Choquet integrals will be interesting tools for the aggregation processes that we are going to carry out.
  • Thumbnail Image
    PublicationOpen Access
    Reduction of the size of L-fuzzy contexts. A tool for differential diagnoses of diseases
    (Taylor & Francis, 2019) Alcalde, Cristina; Burusco Juandeaburre, Ana; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
    Information extraction from an L-fuzzy context becomes a hard problem when we work with a large set of objects and/or attributes. The goal of this paper is to present two different and complementary techniques to reduce the size of the context. First, using overlap indexes, we will establish rankings among the elements of the context that will allow us to determine those that do not provide relevant information and eliminate them. Second, by means of Choquet integrals, we will aggregate some objects or attributes of the context in order to jointly use the provided information. One interesting application of the developed theory consists on helping in the differential diagnoses of diseases that share a large number of symptoms and, therefore, that are difficult of distinguish.