Classification Of Incomplete Patterns Based On The Fusion Of Belief Functions
Zhunga Liu, Quan Pan, Jean Dezert, Arnaud Martin, Grégoire Mercier · Zenodo (CERN European Organization for Nuclear Research) · 2015
The influence of the missing values in the classification of incomplete pattern mainly depends on the context. In this paper, we present a fast classification method for incomplete pattern based on the fusion of belief functions where the missing values are selectively (adaptively) estimated. At first, it is assumed that the missing information is not crucial for the classification, and the object (incomplete pattern) is classified based only on the available attribute values.