Technical data mining with evolutionary radial basis function classifiers
This article deals with two key problems of data mining, the automation of the data mining process and the integration of human domain experts. We show how an evolutionary algorithm (EA) can be used to optimize radial basis function (RBF) neural networks used for classification tasks. First, input features will be chosen from a set of possible input features (feature selection). Second, the number of hidden neurons is adapted (model selection). It is known that interpretable (fuzzy-type) rule sets may be extracted from RBF networks. We show how appropriate training algorithms for RBF networks and penalty terms in the fitness function of the EA may improve the understandability of the extracted rules. The properties of our approach are set out by means of two industrial application examples (process identification and quality control).
data mining evolutionary algorithm feature selection model selection interpretability understandability radial basis function neural network knowledge extraction