Automatically determine initial fuzzy partitions for neuro-fuzzy classifiers

Learning a fuzzy classifier from data is a well-known technique in fuzzy data analysis and many learning algorithms have been proposed, typically in the area of neuro-fuzzy systems. All learning algorithms require a number of parameters to be set by the user. These are typically initial fuzzy partitions for all variables and sometimes also the number of fuzzy rules. Especially, for neuro-fuzzy algorithms the initial choice of parameters can be crucial and if ill-chosen may lead to failure of the learning algorithm. Recent trends in data analysis show that automation is an important issue because it helps to provide advanced analytics to users who are no data analysis experts. In order to fully automate a learning algorithm for fuzzy classifiers we preferably need an algorithm that can determine a suitable initial fuzzy partition for the learning algorithm to start with. In this paper we propose such an algorithm that we have implemented to extend the neuro-fuzzy approach NEFCLASS. NEFCLASS has recently been integrated into an automatic soft computing platform for intelligent data analysis (SPIDA).