Training sample reduction based on association rules for neuro-fuzzy networks synthesis

Abstract

The problem of reduction of training samples for synthesizing diagnostic models has been solved in the paper. The method of dimension reduction of training sample based on association rules has been proposed. It includes the implementation of stages of reduction of instances, features and superfluous terms, uses information on extracted association rules for evaluation of informativeness of features. The proposed method allows to create a partition of feature space with a fewer number of instances compared to the original sample, which in turn makes the synthesis of easier and more convenient for perception diagnostic models possible. The developed method has been implemented in the developed software and was used for the practical problem solving of reduction of training sample for the synthesis of a diagnostic model of confectionery products quality.