The Theory of Belief Functions is One of the Most Important Tools in Information Fusion and Uncertainty Reasoning. Dempster’s Rule of Combination and Its Related Modified Versions Are Used to Combine Independent Pieces of Evidence. However, Until Now Ther
Zhun-ga Liua, Quan Pan, Jean Dezert, Grégoire Mercier, Yong Liu · viXra · 2014
Information fusion technique like evidence theory has been widely applied in the data lassification to improve the performance of classifier. A new fuzzy-belief K-nearest neighbor (FBK-NN) classifier is proposed based on evidential reasoning for dealing with uncertain data. In FBK-NN, each labeled sample is assigned with a fuzzy membership to each class according to its neighborhood. For each input object to classify, K basic belief assignments (BBA’s) are determined from the distances between the object and its K nearest neighbors taking into account the neighbors’ memberships. The K BBA’s are fused by a new method and the fusion results are used to finally decide the class of the query object. FBK-NN method works with credal classification and discriminate specific classes, metaclasses and ignorant class. Meta-classes are defined by disjunction of several specific classes and they allow to well model the partial imprecision of classification of the objects. The introduction of meta-classes in the classification procedure reduces the misclassification errors. The ignorant class is employed for outliers detections. The effectiveness of FBK-NN is illustrated through several experiments with a comparative analysis with respect to other classical methods.