Classification Of Incomplete Patterns Based On The Fusion Of Belief Functions

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.

Related Organizations

Télécom ParisTech
France
University of Rennes 1
France
Northwestern Polytechnical University
China (People's Republic of)
University of Rennes
France

Keywords

classification method, incomplete pattern, classification method

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