The use of a lattice in knowledge processing
The use of a lattice in knowledge processing
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Learning Classification Rules Using Lattices (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Towards a machine learning approach based on incremental concept formation
Intelligent Data Analysis
Formal concept analysis in knowledge discovery: a survey
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Adaptive learning of nominal concepts for supervised classification
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
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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.