A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets

In the field of classification problems, we often encounter classes with a very different percentage of patterns between them, classes with a high pattern percentage and classes with a low pattern percentage. These problems receive the name of ''classification problems with imbalanced data-sets''. In this paper we study the behaviour of fuzzy rule based classification systems in the framework of imbalanced data-sets, focusing on the synergy with the preprocessing mechanisms of instances and the configuration of fuzzy rule based classification systems. We will analyse the necessity of applying a preprocessing step to deal with the problem of imbalanced data-sets. Regarding the components of the fuzzy rule base classification system, we are interested in the granularity of the fuzzy partitions, the use of distinct conjunction operators, the application of some approaches to compute the rule weights and the use of different fuzzy reasoning methods.

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