Maximum likelihood estimation from Uncertain Data in the Belief Function Framework

Thierry Denoeux (1, 2)

1 Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques"
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

We consider the problem of parameter estimation in statistical models in the case where data are uncertain and represented as belief functions. The proposed method is based on the maximization of a generalized likelihood criterion, which can be interpreted as a degree of agreement between the statistical model and the uncertain observations. We propose a variant of the EM algorithm that iteratively maximizes this criterion. As an illustration, the method is applied to uncertain data clustering using finite mixture models, in the cases of categorical and continuous attributes.

Mots clés: Mixture Models; Dempster-Shafer theory; Uncertain data Mining; EM Algorithm; Evidence theory; Clustering; Mixture Models

Domaines: Intelligence artificielle [cs.AI]

Thierry Denoeux. Maximum likelihood estimation from Uncertain Data in the Belief Function Framework. IEEE Transactions on Knowledge and Data Engineering, 2013, 25 (1), pp.119-130. ⟨10.1109/TKDE.2011.201⟩. ⟨hal-00804343⟩