Resample and Combine: An Approach to Improving Uncertainty Representation in Evidential Pattern Classification

Jeanne François (1) , Y. Grandvalet (2) , T. Denoeux (2) , J.M. Roger (1)

1 UMR ITAP - Information – Technologies – Analyse Environnementale – Procédés Agricoles
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

Uncertainty representation is a ma jor issue in pattern recognition. In many applications, the outputs of a classifier do not lead directly to a final decision, but are used in combination with other systems, or as input to an interactive decision process. In such contexts, it may be advantageous to resort to rich and flexible formalisms for rep- resenting and manipulating uncertain information. This paper addresses the issue of uncertainty representation in pattern classification, in the framework of the Dempster-Shafer theory of evidence. It is shown that the quality and reliability of the outputs of a classifier may be improved using a variant of bagging, a resample-and-combine approach introduced by Breiman in a conventional statistical context. This technique is explained and studied experimentally on simulated data and on a character recognition application. In particular, results show that bagging improves classification accuracy and limits the influence of outliers and ambiguous training patterns

Mots clés: EVIDENCE THEORY; BOOTSTRAP; DECISION FUSION; DEMPSTER-SHAFER THEORY; ECHANTILLONNAGE STATISTIQUE; INCERTITUDE; CLASSIFICATION; RECONNAISSANCE DE FORME; SUPERVIDED PATTERN RECOGNITION; K-NARREST NEIGHBOR RULE; BAGGING; CHARACTER RECOGNITION

Domaines: Sciences de l'environnement

Jeanne François, Y. Grandvalet, T. Denoeux, J.M. Roger. Resample and Combine: An Approach to Improving Uncertainty Representation in Evidential Pattern Classification. Information Fusion, 2003, 4 (2), p. 75 - p. 85. ⟨10.1016/S1566-2535(03)00005-8⟩. ⟨hal-00453802⟩