Preference Function Reconstruction for Multiple Criteria Decision Making Based on Machine Learning Approach

Abstract

The problem of expert preference function reconstruction in decision making process of multicriterion comparative assessment of set of object is considered. The problem is reduced to integral indicator identification using available data of object’s performance indexes measurements as well as expert estimation of integral indicators values for each object and feature weights. Based on machine learning approach and expert estimations concordance technique, the solution of preference function recovering problem is obtained in the form of optimal nonlinear object feature convolution.