Abstract
Supervised classification is a spot/task of data mining which consists in building a classifier from a set of examples labeled by their class (learning step) and then predicting the class of new examples with a classifier (classification step). In supervised classification, several approaches were proposed such as: Induction of Decision Trees, and Formal Concept Analysis. The learning of formal concepts is based, generally, on the mathematical structure of Galois lattice (or concept lattice). The complexity of generation of Galois lattice, limits the application fields of these systems. In this paper, we present several methods of supervised classification based on Formal Concept Analysis. We present methods based on concept lattice or sub lattice. We also present the boosting of classifiers, an emerging technique of classification. Finally, we propose the boosting of formal concepts: a new adaptive approach to build only a part of the lattice including the best concepts. These concepts are used as classification rules. Experimental results are given to prove the interest of the proposed method.