Reliability estimation with Extrinsic and Intrinsic measure in belief function theory

Abstract

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.