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Title: A random set and prototype theory approach to rule-based regression
Author: Li , Guanyi
ISNI:       0000 0004 5920 8930
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2014
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In this thesis we explore prototype theory and random set theory as a foundation for antecedent labels in rule-based systems. In particular, we introduce three learning algorithms for regression problems and we also investigate the use of genetic algorithm as a generic parallleter optimization method for each of these approaches. Two of the learning algorithms generate Takagi-Sugeno rules whilst the third corresponds to a form of regression tree. Within the rules fuzzy labels are represented either through a combination of random set and prototype theory or through a novel extension of prototype theory to allow for fuzzy prototypes. Through out the thesis the algorithms are assessed on a number of benchmark data sets and compared with several state-of-the-art regression methods.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available