Paternain Dallo, Daniel
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Paternain Dallo
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Daniel
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Estadística, Informática y Matemáticas
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ISC. Institute of Smart Cities
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Publication Open Access A study of OWA operators learned in convolutional neural networks(MDPI, 2021) Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaOrdered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the expo-nential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.Publication Open Access Some bipolar-preferences-involved aggregation methods for a sequence of OWA weight vectors(Springer, 2021) Jin, LeSheng; Yager, Ronald R.; Chen, Zhen-Song; Špirková, Jana; Paternain Dallo, Daniel; Mesiar, Radko; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaThe ordered weighted averaging (OWA) operator and its associated weight vectors have been both theoretically and practically verified to be powerful and effective in modeling the optimism/pessimism preference of decision makers. When several different OWA weight vectors are offered, it is necessary to develop certain techniques to aggregate them into one OWA weight vector. This study firstly details several motivating examples to show the necessity and usefulness of merging those OWA weight vectors. Then, by applying the general method for aggregating OWA operators proposed in a recent literature, we specifically elaborate the use of OWA aggregation to merge OWA weight vectors themselves. Furthermore, we generalize the normal preference degree in the unit interval into a preference sequence and introduce subsequently the preference aggregation for OWA weight vectors with given preference sequences. Detailed steps in related aggregation procedures and corresponding numerical examples are also provided in the current study.