An extension of the FURIA classification algorithm to low quality data

Abstract

The classification algorithm FURIA (Fuzzy Unordered Rule Induction Algorithm) is extended in this paper to low quality data. An epistemic view of fuzzy memberships is adopted for modeling the incomplete knowledge about training and test sets. The proposed algorithm is validated in different real-world problems and compared to alternative fuzzy rule-based classifiers in both their linguistic understandability and the accuracy of the results. Statistical tests for vague data are used to show that the new algorithm has a competitive edge over previous approaches, especially in some high dimensional problems. © 2013 Springer-Verlag.