In some areas, we expect machinery to be able to simulate behavior,
reasoning ability like human and give human reliable suggestions in the
decision-making process. A prominent feature of human is the ability to reason
on the basis of knowledge formed from life and expressed in natural language.
Because the language characteristic is fuzzy, the first problem that needs to be
solved is how to mathematically formalize the problems of linguistic semantic
and handle semantic language that human often uses in daily life.
In response to those requirements, in 1965, Lotfi A. Zadeh was the first
person to lay the foundation for fuzzy set theory. Based on fuzzy set theory,
Fuzzy Rule Based System (FRBS) has been developed and become one of the
tools of simulating reasoning method and making decisions of human in the
most closely manner. FRBS has been successfully applied in solving practical
problems such as control problem, classification problem, regression problem,
language extraction problem, etc.
When building FRBSs, we need to achieve two goals: accuracy and
interpretability. The thesis will focus on the study of interpretability.
In [1]1 Gacto finds that there are currently two main approaches to
interpretability. The first approach is based on complexity and the second
approach is based on semantics. Another approach proposed by Mencar et. al. in
[2]2, called similar measure function-based approach to assess the
interpretability of semantics-based fuzzy rules. The interpretability of fuzzy
rules is measured by the similarity between knowledge represented by fuzzy set
expression and linguistic expression in natural language.