Argumentation in inductive concept formation

This paper contains a description of methods and algorithms for solving the generalization problem in intelligent decision support systems. For this purpose the argumentation approach for inductive concept formation is used. The methods for finding the conflicts and the generalization algorithm based on the rough set theory are proposed. It is suggested to use the argumentation, based on defeasible reasoning with justification degrees, to improve the quality of the classification models obtained by the generalization algorithm. Noise models in the generalization algorithm are viewed. Experimental results are introduced.