  The theory of belief functions is widely used for data from multiple sources.
Different evidence combination rules have been proposed in this framework
according to the properties of the sources to combine. However, most of these
combination rules are not efficient when there are a large number of sources.
This is due to either the complexity or the existence of an absorbing element
such as the total conflict mass function for the conjunctive based rules when
applied on unreliable evidence. In this paper, based on the assumption that the
majority of sources are reliable, a combination rule for a large number of
sources is proposed using a simple idea: the more common ideas the sources
share, the more reliable these sources are supposed to be. This rule is
adaptable for aggregating a large number of sources which may not all be
reliable. It will keep the spirit of the conjunctive rule to reinforce the
belief on the focal elements with which the sources are in agreement. The mass
on the emptyset will be kept as an indicator of the conflict. The proposed
rule, called LNS-CR (Conjunctive combinationRule for a Large Number of
Sources), is evaluated on synthetic mass functions. The experimental results
verify that the rule can be effectively used to combine a large number of mass
functions and to elicit the major opinion.

Artificial Intelligence