Fuzzy Logic and Information Fusion pp 169-200 | Cite as

# Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification

## Abstract

The use of evolutionary algorithms for designing fuzzy systems provides them with learning and adaptation capabilities, resulting on what is known as Evolutionary Fuzzy Systems. These types of systems have been successfully applied in several areas of Data Mining, including standard classification, regression problems and frequent pattern mining. This is due to their ability to adapt their working procedure independently of the context we are addressing. Specifically, Evolutionary Fuzzy Systems have been lately applied to a new classification problem showing good and accurate results. We are referring to the problem of classification with imbalanced datasets, which is basically defined by an uneven distribution between the instances of the classes. In this work, we will first introduce some basic concepts on linguistic fuzzy rule based systems. Then, we will present a complete taxonomy for Evolutionary Fuzzy Systems. Then, we will review several significant proposals made in this research area that have been developed for addressing classification with imbalanced datasets. Finally, we will show a case study from which we will highlight the good behavior of Evolutionary Fuzzy Systems in this particular context.

## Keywords

Membership Function Fuzzy Rule Minority Class Imbalanced Dataset Fuzzy Label## Notes

### Acknowledgments

This work have been partially supported by the Spanish Ministry of Science and Technology under projects TIN-2012-33856, TIN2014-57251-P; the Andalusian Research Plans P12-TIC-2958, P11-TIC-7765 and P10-TIC-6858; and both the University of Jaén and Caja Rural Provincial de Jaén under project UJA2014/06/15.

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