Classification with imbalanced data-sets is one of the recent challenging problems in Data Mining. In this framework, the
class distribution is not uniform and the separability between the classes is often difficult. From the available techniques
in the Machine Learning field, we focus on the use of Fuzzy Rule Based Classification Systems, as they provide an interpretable
model for the end user by means of linguistic variables.
The aim of this work is to increase the performance of fuzzy modeling by adding a higher degree of knowledge by means of the
use of Interval-valued Fuzzy Sets. Furthermore, we will contextualize the Interval-valued Fuzzy Sets with a post-processing
genetic tuning of the amplitude of their upper bounds in order to enhance the global behaviour of this methodology.
Keywords Fuzzy Rule-Based Classification Systems - Interval-valued Fuzzy Sets - Tuning - Genetic Algorithms - Imbalanced Data-Sets