Integration of case-based and rule-based reasoning through fuzzy inference in decision support systems

In the present paper we present an approach to facilitation of decision-making in the decision support systems (DSS). The approach is based on the transformation of knowledge from the implicit presentation in the form of cases stored in the case bases to the explicit presentation in the form of rules stored in the rule bases. The efficient method of decision-making on the basis of cases classification has been proposed. The method is based on the original algorithm of transformation of cases (precedents) sample to the set of linguistic rules allowing to make relevant decisions. Research of the method has shown good accuracy of the decisions classification on test data and dependence of the accuracy on the number of membership functions and the procedure of resolving conflicts between the rules. (C) 2017 The Authors. Published by Elsevier B.V.