Extending a simple genetic cooperative-competitive learning fuzzy classifier to low quality datasets

Exploiting the information in low quality datasets has been recently acknowledged as a new challenge in Genetic Fuzzy Systems. Owing to this, in this paper we discuss the basic principles that govern the extension of a fuzzy rule based classifier to interval and fuzzy data. We have also applied these principles to the genetic learning of a simple cooperative-competitive algorithm, that becomes the first example of a Genetic Fuzzy Classifier able to use low quality data. Additionally, we introduce a benchmark, comprising some synthetic samples and two real-world problems that involve interval and fuzzy-valued data, that can be used to assess future algorithms of the same kind.