Interpretable fuzzy systems modeling with cooperation between expert and induced knowledge (Modelado de sistemas borrosos interpretables con cooperación entre conocimiento experto e inducido)

Resumen

This thesis describes a new methodology for making easier the design process of interpretable knowledge bases. It considers both expert knowledge and knowledge extracted from data. The combination of both kinds of knowledge is likely to yield robust compact systems with a good trade-off between accuracy and interpretability. Fuzzy logic offers an integration framework where both types of knowledge are represented using the same formalism. However, as two knowledge bases may convey contradictions and/or redundancies, the integration process must be made carefully. Results obtained, in several well-known benchmark classification problems, show that our methodology leads to highly interpretable knowledge bases with a good accuracy, comparable to that achieved by other methods. Moreover, the new methodology proposed was applied to some real-world applications.

All results presented in this work were reached using a free software tool (distributed under the terms of the GPL license) for generating and refining fuzzy knowledge bases. It was designed and developed as an important part of the thesis. It has been used as a test bed in order to check all theoretical aspects of the thesis.