A multiple classifier system using ambiguity rejection for clustering-classification cooperation

This article aims at showing that supervised and unsupervised learnings are not competitive, but complementary methods. We propose to use a fuzzy clustering method with ambiguity rejection to guide the supervised learning performed by bayesian classifiers. This method detects ambiguous or mixed areas of a learning set. The problem is seen from the multi-decision point of view (i.e. several classification modules). Each classification module is specialized on a particular region of the feature space. These regions are obtained by fuzzy clustering and constitute the original data set by union. A classifier is associated with each cluster. The training set for each classifier is then defined on the cluster and its associated ambiguous clusters. The overall system is parallel, since different classifiers work with their own training data clusters. The algorithm makes possible the adaptive classifier selection in the sense that the fuzzy clustering with ambiguity rejection gives adapted training data regions of the feature space. The decision making is the fusion of outputs from the most adapted classifiers.