Laboratoire de Génie Informatique et d’Automatique de l’Artois


Pattern classification based on belief function theory

The 19 June 2017 at 14:00 Seminars room of the LGI2A, FSA, Béthune
Zhunga LIU Associate professor Northwestern Polytechnical University, Xi'an, China

In the complex pattern classification problem, how to well deal with the uncertainty remains an interesting and important research topic. Belief function theory provides an efficient tool to represent and combine the uncertain information, and it is employed here for pattern classification. In this talk, we will present three parts of our recent work on the belief-based pattern classification:

  1. credal classification of incomplete pattern with missing attributes
  2. credal c-means clustering method for the partly overlapping data and
  3. classifier fusion with refined reliability evaluation under the power-set.

We will also discuss some potential research work with belief functions in the final.