Mining multi-class industrial data with evolutionary fuzzy rules
Pavel Krömer, Jan Platoš, Václav Snåšel · 2013
Methods based on fuzzy sets and fuzzy logic have proved to be efficient data classifiers and value estimators. This study presents an application of evolutionary evolved fuzzy rules based on the concept of extended Boolean queries to a multi-class data mining problem. Fuzzy rules are used as symbolic classifiers machine-learned from the data and used to label data samples and predict the value of an output variable. The output variable can be both a label (category) and a continuous value. This study presents an application of evolutionary fuzzy rules to the prediction of multi-class quality attributes in an industrial data set and compares the prediction obtained by fuzzy rules to the prediction achieved by support vector machines.