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Callejas Bedregal, Benjamin

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https://academica-e.unavarra.es/handle/2454/48690

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Callejas Bedregal

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Benjamin

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IngenierĆ­a ElĆ©ctrica, ElectrĆ³nica y de ComunicaciĆ³n

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0000-0002-6757-7934

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811677

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2015 - 20204
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Subject: Choquet integral Ɨ

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Now showing 1 - 4 of 4
  • Thumbnail Image
    PublicationOpen Access
    d-Choquet integrals: Choquet integrals based on dissimilarities
    (Elsevier, 2020) Bustince Sola, Humberto; Mesiar, Radko; FernĆ”ndez FernĆ”ndez, Francisco Javier; Galar Idoate, Mikel; Paternain Dallo, Daniel; Altalhi, A. H.; Pereira Dimuro, GraƧaliz; Callejas Bedregal, Benjamin; TakĆ”Ä, Zdenko; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; EstadĆ­stica, InformĆ”tica y MatemĆ”ticas; Universidad PĆŗblica de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA13
    The paper introduces a new class of functions from [0,1]n to [0,n] called d-Choquet integrals. These functions are a generalization of the 'standard' Choquet integral obtained by replacing the difference in the definition of the usual Choquet integral by a dissimilarity function. In particular, the class of all d-Choquet integrals encompasses the class of all 'standard' Choquet integrals but the use of dissimilarities provides higher flexibility and generality. We show that some d-Choquet integrals are aggregation/pre-aggregation/averaging/functions and some of them are not. The conditions under which this happens are stated and other properties of the d-Choquet integrals are studied.
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    PublicationOpen Access
    Improving the performance of fuzzy rule-based classification systems based on a nonaveraging generalization of CC-integrals named C-F1F2-integrals
    (IEEE, 2019) Lucca, Giancarlo; Pereira Dimuro, GraƧaliz; FernƔndez FernƔndez, Francisco Javier; Bustince Sola, Humberto; Callejas Bedregal, Benjamin; Sanz Delgado, JosƩ Antonio; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadƭstica, InformƔtica y MatemƔticas
    A key component of fuzzy rule-based classification systems (FRBCS) is the fuzzy reasoning method (FRM) since it infers the class predicted for new examples. A crucial stage in any FRM is the way in which the information given by the fired rules during the inference process is aggregated. A widely used FRM is the winning rule, which applies the maximum to accomplish this aggregation. The maximum is an averaging operator, which means that its result is within the range delimited by the minimum and the maximum of the aggregated values. Recently, new averaging operators based on generalizations of the Choquet integral have been proposed to perform this aggregation process. However, the most accurate FRBCSs use the FRM known as additive combination that considers the normalized sum as the aggregation operator, which is nonaveraging. For this reason, this paper is aimed at introducing a new nonaveraging operator named C-F1F2-integral, which is a generalization of the Choquet-like Copula-based integral (CC-integral). C-F1F2-integrals present the desired properties of an aggregation-like operator since they satisfy appropriate boundary conditions and have some kind of increasingness property. We show that C-F1F2 -integrals, when used to cope with classification problems, enhance the results of the previous averaging generalizations of the Choquet integral and provide competitive results (even better) when compared with state-of-the-art FRBCSs.
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    PublicationOpen Access
    Pre-aggregation functions: construction and an application
    (IEEE, 2015) Lucca, Giancarlo; Sanz Delgado, JosĆ© Antonio; Pereira Dimuro, GraƧaliz; Callejas Bedregal, Benjamin; Mesiar, Radko; KolesĆ”rovĆ”, Anna; Bustince Sola, Humberto; AutomĆ”tica y ComputaciĆ³n; Automatika eta Konputazioa
    In this work we introduce the notion of preaggregation function. Such a function satisfies the same boundary conditions as an aggregation function, but, instead of requiring monotonicity, only monotonicity along some fixed direction (directional monotonicity) is required. We present some examples of such functions. We propose three different methods to build pre-aggregation functions. We experimentally show that in fuzzy rule-based classification systems, when we use one of these methods, namely, the one based on the use of the Choquet integral replacing the product by other aggregation functions, if we consider the minimum or the Hamacher product t-norms for such construction, we improve the results obtained when applying the fuzzy reasoning methods obtained using two classical averaging operators like the maximum and the Choquet integral.
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    PublicationOpen Access
    A proposal for tuning the Ī± parameter in CĪ±C-integrals for application in fuzzy rule-based classification systems
    (Springer, 2020) Lucca, Giancarlo; Sanz Delgado, JosƩ Antonio; Pereira Dimuro, GraƧaliz; Callejas Bedregal, Benjamin; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadƭstica, InformƔtica y MatemƔticas
    In this paper, we consider the concept of extended Choquet integral generalized by a copula, called CC-integral. In particular, we adopt a CC-integral that uses a copula defined by a parameter Ī±, which behavior was tested in a previous work using different fixed values. In this contribution, we propose an extension of this method by learning the best value for the parameter Ī± using a genetic algorithm. This new proposal is applied in the fuzzy reasoning method of fuzzy rule-based classification systems in such a way that, for each class, the most suitable value of the parameter Ī± is obtained, which can lead to an improvement on the system's performance. In the experimental study, we test the performance of 4 different so called CĪ±C-integrals, comparing the results obtained when using fixed values for the parameter Ī± against the results provided by our new evolutionary approach. From the obtained results, it is possible to conclude that the genetic learning of the parameter Ī± is statistically superior than the fixed one for two copulas. Moreover, in general, the accuracy achieved in test is superior than that of the fixed approach in all functions. We also compare the quality of this approach with related approaches, showing that the methodology proposed in this work provides competitive results. Therefore, we demonstrate that CĪ±C-integrals with Ī± learned genetically can be considered as a good alternative to be used in fuzzy rule-based classification systems.