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
Learning Classification Rules Using Lattices (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Quality Measures in Data Mining (Studies in Computational Intelligence)
Quality Measures in Data Mining (Studies in Computational Intelligence)
Towards a machine learning approach based on incremental concept formation
Intelligent Data Analysis
Boosting Formal Concepts to Discover Classification Rules
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Diversity analysis on boosting nominal concepts
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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In recent decades, several machine learning methods based on Formal Concept Analysis have been proposed. The learning process is based on the construction of the mathematical structure of the Galois lattice. Two major limits characterize these methods. First, most of them are limited to the binary data processing. Second, the exponential complexity of a Galois lattice generation limits their fields of application. In this paper, we consider the Boosting of classifiers, which is an adaptive approach of classification. We propose the Boosting of classifiers based on Nominal Concepts. This method builds part of the lattice including the best concepts (pertinent concepts). It is distinguished from the other methods based on Formal Concept Analysis by its ability to handle nominal data. The discovered concepts are called Nominal Concepts and they are used as classification rules. The comparative studies and the experimental results carried out, prove the interest of this method compared to those existing in literature.