Approximate Reasoning in Supervised Classification Systems
Hamid Seridi*,**, Herman Akdag**, Rachid Mansouri***,
and Mohamed Nemissi*
*Laboratoire d’Automatique et Informatique de Guelma (LAIG), Université 08 mai 1945 de Guelma, B.P. 401 Guelma 24000, Algeria
**Laboratoire d’Etude et de Recherche en Informatique (LERI), Université de Reims Champagne Ardenne, Rue des Grayères BP1035 51687 Reims Cedex2, France
***Laboratoire de Genie Civil et D’hydraulique (LGCH), Université 08 mai 1945 de Guelma, B.P. 401 Guelma 24000, Algeria
In knowledge-based systems, uncertainty in propositions can be represented by various degrees of belief encoded by numerical or symbolic values. The use of symbolic values is necessary in areas where the exact numerical values associated with a fact are unknown by experts. In this paper we present an expert system of supervised automatic classification based on a symbolic approach. This last is composed of two sub-systems. The first sub-system automatically generates the production rules using training set; the generated rules are accompanied by a symbolic degree of belief which characterizes their classes of memberships. The second is the inference system, which receives in entry the base of rules and the object to classify. Using classical reasoning (Modus Ponens), the inference system provides the membership class of this object with a certain symbolic degree of belief. Methods to evaluate the degree of belief are numerous but they are often tarnished with uncertainty. To appreciate the performances of our symbolic approach, tests are made on the Iris data basis.
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