Reliability estimation with Extrinsic and Intrinsic measure in belief function theory

Belief function theory provides a robust framework for uncertain information modeling. It also offers several fusion tools in order to profit from multi-source context. Nevertheless, fusion is a sensible task where conflictual information may appear especially when sources are unreliable. In belief function theory, a classical approach would estimate the source´s reliability before any discounting operation. Existing solutions for source´s reliability estimation, are based on the assumption that distance is the only factor for conflictual situations. Indeed, integrating only distance measures to estimate source´s reliability is not sufficient where source´s confusion may also be considered as conflict origin. In this paper, we tackle reliability estimation and we introduce a new discounting operator that considers those two possible conflict origins. The proposed approach is applied on benchmark data for classification purpose.