Neuro-fuzzy learning with symbolic and numeric data

In real world datasets we often have to deal with different kinds of variables. The data can be, for example, symbolic or numeric. Data analysis methods can often deal with only one kind of data. Even when fuzzy systems are applied - which are not dependent on the scales of variables - usually only numeric data is considered. In this paper we present learning algorithms for creating fuzzy rules and training membership functions from data with symbolic and numeric variables. The algorithms are exentions to our neuro-fuzzy classification approach NEFCLASS. We also demonstrate the applicability of the algorithms on two real-world datasets.