On the Usefulness of Interval Valued Fuzzy Sets for Learning Fuzzy Rule Based Classification Systems

Extended Abstract

One of the main advantages of the Fuzzy Rule Based Classification Systems (FRBCSs) is the high interpretability of the model. However, the disadvantage of these systems may be their lack of accuracy when dealing some complex systems, due to the inflexibility of the concept of linguistic variable, which imposes hard restrictions to the fuzzy rule structure. For example, sometimes when the classes are overlapped, we have not exact knowledge about the membership degree of some elements to the fuzzy sets that characterize the attributes defining the class.

This situation suggests the possibility to represent the membership degrees of the objects to the fuzzy set by means of an interval. That is, to employ the Interval- Valued Fuzzy Sets (IVFSs) to characterize the linguistic labels that compound the attributes of the problems. IVFSs allow us to take into account the effect of the ignorance of the experts in the membership function definition.

The aim of this talk is to shown the performance of FRBCSs by extending the Knowledge Base with the application of the concept of IVFSs. The modeling of the linguistic labels by means of IVFSs implied an adaptation of the original fuzzy reasoning method to allow us to handle the uncertainty that is inherent to the definition process of the membership functions. We define new reasoning methods meaning use of the interval-valued restricted equivalence functions to increase the relevance of the rules in which the equivalence of the interval membership degrees of the patterns and the ideal membership degrees is greater, which is a desirable behavior. Furthermore, the parametrized construction of this fuzzy reasoning method allows the choice of the optimal function for each variable to be performed, which could involve a potential improvement of the system behavior. These parameters will be tuned using genetic algorithms in order to further improve the performance of the systems in a general framework.

We will show different experimental studies showing the usefulness of the IVFSs for enhancing the FRBCSs performance.