DEVELOPMENT OF TECHNIQUE FOR DETERMINING THE MEMBERSHIP FUNCTION VALUES ON THE BASIS OF GROUP EXPERT ASSESSMENT IN FUZZY DECISION TREE METHOD - Shved, A. V. (Radio Electronics, Computer Science, Control, 2024)
Context. Recently, fuzzy decision trees have become widely used in solving the classification problem. In the absence of objective information to construct the membership function that shows the degrees of belongingness of elements to tree nodes, the only way to obtain information is to involve experts. In the case of group decision making, the task of aggregation of experts’ preferences in order to synthesize a group decision arises. The object of the study is group expert preferences of the degree of belonging (membership function) of an element to a given class, attribute, which require structuring and aggregation in the process of construction and analysis of a fuzzy decision tree. Objective. The purpose of the article is to develop a methodology for determining the membership degree of elements to a given class (attribute) based on the group expert assessment in the process of construction and analysis of fuzzy decision trees. Method. The research methodology is based on the complex application of the mathematical apparatus of the theory of plausible and paradoxical reasoning and methods of fuzzy logic to solve the problem of aggregating fuzzy judgments of the classification attribute values in the process of construction and analysis of a fuzzy decision tree. The proposed approach uses the mechanism of combination of expert evidences (judgments), formed within the framework of the Dezert-Smarandache hybrid model, based on the PCR5 proportional conflict redistribution rule to construct a group solution. Results. The issues of structuring fuzzy expert judgments are considered and the method of synthesis of group expert judgments regarding the values of membership degree of elements to classification attributes in the process of construction and analysis of fuzzy decision trees has been proposed. Conclusions. The models and methods of structuring and synthesis of group decisions based on fuzzy expert information were further developed. In contrast to the existing expert methods for the construction of membership function in context of group decision making, the proposed approach allows synthesizing a group decision taking into account the varying degree of conflict mass in the process of combination of original expert evidenced. This approach allows to correctly aggregate both agreed and contradictory (conflicting) expert judgments.