- 221 views
- 781 downloads
Fuzzy Rule-Based Systems: Design, Analysis, and Applications
-
- Author / Creator
- Kerr-Wilson, Jeremy
-
The extraction of knowledge from data is a relatively recent computational pursuit which has been the focus of significant research attention and has an extensive field of potential applications. With the advent of widespread data collection describing a variety of systems spanning many fields of expertise, the extraction of useful knowledge from data, for prediction or insight, has extraordinary potential and significant value. This realization has led to the development of machine learning, a collection of computational processes and algorithms through which sophisticated computer models can be constructed on the basis of data. Human centric systems, and more specifically certain forms of fuzzy systems, are a notable subset of machine learning which focus on knowledge extraction using multi-valued logic and set theory. Fuzzy systems are particularly well-suited to human-centric modelling as fuzzy sets describe real world systems, as perceived by humans, much more accurately than binary models. Fuzzy rule-based models are a form of fuzzy model which is particularly well-suited to human centric tasks due to the high degree of readability and interpretability conveyed to an expert reader. This, combined with their strong predictive ability, makes fuzzy rule-based systems an excellent candidate for those computational modelling pursuits where predictive accuracy may not be the singular requirement of a model. The objective of this dissertation is to design, analyze, and develop novel applications, methodologies, and algorithms for use with fuzzy rule-based systems, seeking to further their utility in predictive and human-centric modelling. In this dissertation, fuzzy rule-based systems are applied to different problem types, combined with existing computational data-structures and architectures, extracted from data in novel manners and formats, and analyzed to assess certain aspects of rule quality. Acknowledging the critical role of human centricity in computational modelling, we develop a set of fuzzy rule stability criteria which aim to quantify aspects of fuzzy rule quality while capturing critical non-numerical aspects of rule quality such as repeatability, consistency, and generalizability. We examine the generation of fuzzy rule-based models using hierarchical clustering and extract granular fuzzy models from data, forming information granules in the consequent parts of the fuzzy rules. We make use of fuzzy rule-based systems as the component models (weak learners) of a boosted ensemble, exploring their predictive power and adaptability in this environment. Finally, a novel fuzzy rule architecture is proposed using a hierarchical structure, alongside a generation procedure for extracting this hierarchical structure form data, with the aim of improving predictive performance and increasing the interpretability of the system.Each of these topics are justified with extensive experimental studies, using real-world data sets available from public repositories, demonstrating the feasibility or superiority of the proposed methods as compared to existing methodologies.
-
- Graduation date
- Spring 2019
-
- Type of Item
- Thesis
-
- Degree
- Doctor of Philosophy
-
- License
- Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.