Parallel Learning and Classification for Rules based on Formal Concepts

Abstract

Supervised classification is a spot/task of data mining which consist on building a classifier from a set of instances labeled with their class (learning step) and then predicting the class of new instances with a classifier (classification step). In supervised classification, several approaches were proposed such as: Induction of Decision Tree and Formal Concept Analysis. The learning of formal concepts is generally based on the mathematical structure of Galois lattice (or concept lattice). The complexity of Galois lattice generation limits the application fields of these systems. In this paper, we discuss about supervised classification based on Formal Concept Analysis and we present methods based on concept lattice or sub lattice. We propose a new approach that builds only a part of the lattice, including the best concepts (i.e pertinent concepts). These concepts are used as classifiers in parallel combination using voting rule. The proposed method is based on Dagging of Nominal Classifier. Experimental results are given to prove the interest of the proposed method.