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Title: From approximative to descriptive fuzzy models
Author: Marín-Blázquez, Javier G.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2003
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This thesis presents an effective and efficient approach for translating fuzzy classification rules that use approximative sets to rules that use descriptive sets and linguistic hedges of predefined meaning. It works by first generating rules that use approximative sets from training data and then translating the resulting approximative rules into descriptive ones. The translation is implemented with multi-objective GAs. Hedges that are useful for supporting such translations are provided. This thesis presents an improved version of more effective hedges specifically devised for trapezoidal fuzzy sets, to be applied to dilate or concentrate a given set by expanding or shrinking its constituent parts. It also introduces three new hedges not existing in the literature. The translated rules are functionally equivalent to the original approximative ones, or a close equivalent given search time restrictions, while reflecting their underlying preconceived meaning. Thus, fuzzy descriptive classifiers can be obtained by taking advantage of any existing approach to approximative modelling which is generally efficient and accurate, whilst employing rules that are comprehensible to human users.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available