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 misclassification 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. 
Evidential classifier for imprecise data based on belief functions