Title :
Weighted linguistic modelling based on fuzzy subsethood values
Author :
Rasmani, K.A. ; Shen, Q.
Author_Institution :
Sch. of Informatics, Edinburgh Univ., UK
Abstract :
A basic aim of the development of fuzzy linguistic models is to produce fuzzy systems which have both a high accuracy rate and a high degree of transparency. This paper presents a modelling method which allows the creation of accurate fuzzy linguistic models, based on fuzzy subsethood-values. A resulting model is represented in the form of weighted fuzzy general rules, employing relative weights generated from fuzzy subsethood values. These weights are adjustable according to the datasets available for learning. The effectiveness of this work is demonstrated with experimental comparative studies.
Keywords :
computational linguistics; data mining; fuzzy logic; fuzzy set theory; knowledge based systems; learning (artificial intelligence); Iris-Plant dataset; Saturday Morning Problem dataset; data-driven learning; fuzzy linguistic models; fuzzy rule-based systems; fuzzy subsethood values; high accuracy rate; high degree of transparency; relative weights; weighted fuzzy general rules; weighted linguistic modelling; Buildings; Classification tree analysis; Decision trees; Fuzzy sets; Fuzzy systems; Humans; Informatics; Knowledge based systems; Particle measurements; Training data;
Conference_Titel :
Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on
Print_ISBN :
0-7803-7810-5
DOI :
10.1109/FUZZ.2003.1209451
