PROCEEDINGS IPMU '08
Adapting a Combination Rule to Non-Independent Information
B. Quost, T. Denoeux, M.-H. Masson
In this article, we address the combination
of non-independent sources
to solve classification problems,
within the theory of belief functions.
We show that the cautious
rule of combination [1, 2] is well-suited
to such problems. We propose
a method to learn the combination
rule from training data, and
we generalize it in the case of complex
dependence of the sources. We
demonstrate the validity of our approach
on several synthetic and realdata
sets.
 | PDF full paper |