Authors:
Mohamed Saïd Bouguelid
;
Moamar Sayed Mouchaweh
and
Patrice Billaudel
Affiliation:
Centre de Recherche en Science et Technologie de l’Information (CReSTIC), Université de Reims-Champagne-Ardennes, France
Keyword(s):
Pattern recognition, Fuzzy pattern matching, Nearest Neighbours techniques, Multi-criteria decision.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Decision Support Systems
;
Enterprise Information Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Intelligent Fault Detection and Identification
;
Knowledge-Based Systems Applications
;
Machine Learning in Control Applications
Abstract:
We use the classification method Fuzzy Pattern Matching (FPM) to realize the industrial and medical diagnosis. FPM is marginal, i.e., its global decision is based on the selection of one of the intermediate decisions. Each intermediate decision is based on one attribute. Thus, FPM does not take into account the correlation between attributes. Additionally, FPM considers the shape of classes as convex one. Finally the classes are considered as equi-important by FPM. These drawbacks make FPM unusable for many real world applications. In this paper, we propose improving FPM to solve these drawbacks. Several synthetic and real data sets are used to show the performances of the Improved FPM (IFPM) with respect to classical one as well as to the well known classification method K Nearest Neighbours (KNN). KNN is known to be preferment in the case of data represented by correlated attributes or by classes with different a priori probabilities and non convex shape.