Credal Classification of Uncertain Data Using Belief Functions

Abstract

A credal classification rule (CCR) is proposed to deal with the uncertain data under the belief functions framework. CCR allows the objects to belong to not only the specific classes, but also any set of classes (i.e. meta-class) with different masses of belief. In CCR, each specific class is characterized by a class center. Specific class consists of the objects that are very close to the center of this class. A meta-class is used to capture imprecision of the class of the object that is simultaneously close to several centers of specific classes and hard to be correctly committed to a particular class. The belief assignment of the object to a meta-class depends both on the distances to the centers of the specific class included in the meta-class, and on the distance to the meta-class center. Some objects too far from the others will be considered as outliers (noise). CCR provides the robust classification results since it reduces the risk of misclassification errors by increasing the non-specificity. The effectiveness of CCR is illustrated by several experiments using artificial and real data sets.