Classification of uncertain and imprecise data based on evidence theory
作者： Zhun-ga Liu , Quan Pan , Jean Dezert
DOI: 10.1016/J.NEUCOM.2013.12.009
关键词:
摘要: In this paper, we present a new belief c× K neighbor (BCKN) classifier based on evidence theory for data classification when the available attribute information appears insufficient to correctly classify objects in specific classes. In BCKN, the query object is classified according to its K nearest neighbors in each class, and c× K neighbors are involved in the BCKN approach (c being the number of classes). BCKN works with the credal classification introduced in the belief function framework. It allows to commit, with different masses of …
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