Franco, CamiloMontero, JavierRodríguez, Juan TinguaroSun, FuchunLi, TianruiLI, Hongbo2023-06-192023-06-192014Amo A, Montero J, Biging G, Cutello V (2004) Fuzzy classification systems. Eur J Oper Res 156:495–507 Cacioppo J, Gardner W, Berntson G (1997) Beyond bipolar conceptualizations and measures: the case of attitudes and evaluative space. Pers Soc Psychol Rev 1:3–25 Montero J, Gómez D, Bustince H (2007) On the relevance of some families of fuzzy sets. Fuzzy Sets Syst 158:2429–2442 Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47:263–291 Keil A, Stolarova M, Moratti S, Ray W (2007) Adaptation in human visual cortex as a mechanism for rapid discrimination of aversive stimuli. NeuroImage 36:472–479 O’Doherty J, Kringelback M, Rolls E, Hornak J, Andrews C (2001) Abstract reward and punishment representations in the human orbitofrontal cortex. Nat Neurosci 4:95–102 Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20:87–96 Franco C, Montero J, Rodríguez JT (2011) On partial comparability and fuzzy preference-aversion models. In: Proceedings of the ISKE conference, Shangai, paper 1448 15–17 December Franco C, Montero J, Rodríguez JT (2012) A fuzzy and bipolar approach to preference modeling with application to need and desire. Fuzzy Sets Syst 214:20–34.doi:10.1016/j.fss.2012.06.006 Kaplan K (1972) On the ambivalence-indifference problem in attitude theory and measurement: a suggested modification of the semantic differential technique. Psychol Bull 77:361–372 Osgood Ch, Suci G, Tannenbaum P (1958) The measurement of meaning. University of Illinois Press, Urbana Benferhat S, Dubois D, Kaci S, Prade H (2006) Bipolar possibility theory in preference modeling: representation, fusion and optimal solutions. Inform Fus 7:135–150 Bonnefon JF, Dubois D, Fargier H, Leblois S (2008) Qualitative heuristics for balancing the pros and the cons. Theor Decis 65:71–95 Dubois D, Prade H (2008) An introduction to bipolar representations of information and preference. Int J Intell Syst 23:866–877 Rodríguez JT, Franco C, Montero J (2011) On the relationship between bipolarity and fuzziness. In: Proceedings of the EUROFUSE conference, Régua, 193–205,September 21–23 Fodor J, Roubens M (1994) Fuzzy preference modelling and multicriteria decision support. Kluwer Academic, Dordrecht Montero J, Tejada J, Cutello C (1997) A general model for deriving preference structures from data. Eur J Oper Res 98:98–110 Roubens M, Vincke Ph (1985) Preference modeling. Springer, Berlin Van der Walle B, de Baets B, Kerre E (1998) Characterizable fuzzy preference structures. Ann Oper Res 80:105–136 Goguen J (1969) The logic of inexact concepts. Synthese 19:325–373 Zadeh L (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci 8:199–249 Franco C, Montero J (2010) Organizing information by fuzzy preference structures—Fuzzy preference semantics. In: Proceedings of the ISKE conference, Hangzhou, 135–140, 15–16 November Franco C, Rodríguez JT, Montero J (2010) Information measures over intuitionistic four valued fuzzy preferences. In: Proceedings of the IEEE-WCCI, Barcelona, 18–23 July, 1971–1978 Scheweizer B, Sklar A (1983) Probabilistic metric spaces. North-Holland, Amsterdam Keynes J (1963) A treatise on probability. MacMillan, London Yager R (2007) Relevance in systems having a fuzzy-set-based semantics. Int J Intell Syst 22:385–396 Yager R, Petry F (2005) A framework for linguistic relevance feedback in content-based image retrieval using fuzzy logic. Inf Sci 173:337–352 Birkhoff G (1964) Lattice theory. American Mathematical Society, Providence Gonzalez-Pachón J, Gómez D, Montero J, Yáñez J (2003) Soft dimension theory. Fuzzy Sets Syst 137:137–149 CrossRef Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5:297–323978-3-642-37831-710.1007/978-3-642-37832-4_11https://hdl.handle.net/20.500.14352/35717Proceedings of the Seventh International Conference on Intelligent Systems and Knowledge Engineering, Beijing, China, Dec 2012 (ISKE 2012)Abstract Fuzzy preference and aversion relations allow measuring in a gradual manner the attitude of the individual regarding some pair of alternatives. Following the Preference-Aversion (PA) model, previously introduced for identifying the subjective cognitive state for some decision situation; here, we explore a methodology for learning relevance degrees over the complete system of alternatives. In this way it is possible to identify in a quick way, the pieces of information that are more important for solving a given decision problem.spaRelevance in preference structuresbook parthttp://link.springer.com/chapter/10.1007%2F978-3-642-37832-4_11http://link.springer.com/restricted access510.5Preference structuresPeference-aversionRelevanceLógica simbólica y matemática (Matemáticas)1102.14 Lógica Simbólica