TY - JOUR
T1 - Learning preferences from paired opposite-based semantics
AU - Franco de los Ríos, Camilo
AU - Rodríguez, J. Tinguaro
AU - Montero, Javier
PY - 2017
Y1 - 2017
N2 - Preference semantics examine the meaning of the preference predicate, according to the way that alternatives can be understood and organized for decision making purposes. Through opposite-based semantics, preference structures can be characterized by their paired decomposition of preference into opposite poles, and their respective valuation of binary preference relations. Extending paired semantics by fuzzy sets, preference relations can be represented in a gradual functional form, under an enhanced representational frame for examining the meaning of preference. Following a semantic argument on the character of opposition, the compound meaning of preference emerges from the fuzzy reinforcement of paired opposite concepts, searching for significant evidence for affirming dominance among the decision objects. Here we propose a general model for the paired decomposition of preference, examining its characteristic semantics under a binary and fuzzy logical frame, and identifying solutions with different values of significance for preference learning.
AB - Preference semantics examine the meaning of the preference predicate, according to the way that alternatives can be understood and organized for decision making purposes. Through opposite-based semantics, preference structures can be characterized by their paired decomposition of preference into opposite poles, and their respective valuation of binary preference relations. Extending paired semantics by fuzzy sets, preference relations can be represented in a gradual functional form, under an enhanced representational frame for examining the meaning of preference. Following a semantic argument on the character of opposition, the compound meaning of preference emerges from the fuzzy reinforcement of paired opposite concepts, searching for significant evidence for affirming dominance among the decision objects. Here we propose a general model for the paired decomposition of preference, examining its characteristic semantics under a binary and fuzzy logical frame, and identifying solutions with different values of significance for preference learning.
KW - Fuzzy logic
KW - Fuzzy reinforcement
KW - Paired concepts
KW - Preference structures
KW - Semantic opposition
KW - Significance
U2 - 10.1016/j.ijar.2017.04.010
DO - 10.1016/j.ijar.2017.04.010
M3 - Journal article
AN - SCOPUS:85019119714
SN - 0888-613X
VL - 86
SP - 80
EP - 91
JO - International Journal of Approximate Reasoning
JF - International Journal of Approximate Reasoning
ER -