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Evidential classifier for imprecise data based on belief functions

机译:基于信念函数的不精确数据的证据分类器

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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.
机译:本文开发了一种基于信念函数的新证据分类器(EC),用于使用K近邻对不精确数据进行分类。 EC与credal分类一起使用,它可以将对象分类为特定类,由几个特定类的并集定义的元类或无知类中的异常检测类。 EC的主要思想是,只要某个对象同时接近几个无法区分的类,就不要将其分类。在这种情况下,EC会将对象与适当的元类相关联,以减少错误分类错误。完全无知类被解释为代表所有与其他数据相距太远的对象的离群值类。与对象关联的K个基本信念分配(bba's)由对象到其K最近邻居的距离和一些选定的不精确阈值确定。对象的分类取决于这些K bba的整体组合结果。通过基于人工和真实数据集的几个示例,说明了这种新的证据分类器相对于其他经典方法的兴趣和潜力。

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