Qualitative classification and evaluation in possibilistic decision trees

Abstract

This paper presents a method for classifying objects in an uncertain context using decision trees. Uncertainty is related to attributes' values of objects to classify and is handled in a qualitative possibilistic framework. Then, an evaluation method to judge the classification efficiency, in an uncertain context, is proposed.