Evidential classifier for imprecise data based on belief functions

摘要

A new evidential classifier (EC) based on belief functions is developed in this paper for the classification of imprecise data using K-nearest neighbors. EC works with credal classification which allows to classify the objects either in the specific classes, in the meta-classes defined by the union of several specific classes, or in the ignorant class for the outlier detection. The main idea of EC is to not classify an object in a particular class whenever the object is simultaneously close to several classes that turn to be indistinguishable for it. In such case, EC will associate the object with a proper meta-class in order to reduce the misclassi-fication errors. The full ignorant class is interpreted as the class of outliers representing all the objects that are too far from the other data. The K basic belief assignments (bba's) associated with the object are determined by the distances of the object to its K-nearest neighbors and some chosen imprecision thresholds. The classification of the object depends on the global combination results of these K bba's. The interest and potential of this new evidential classifier with respect to other classical methods are illustrated through several examples based on artificial and real data sets.