Abstract
In recent decades, several machine learning methods based on F ormal C oncept A nalysis 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 F ormal C oncept A nalysis 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.
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Meddouri, N., Maddouri, M. (2010). Adaptive Learning of Nominal Concepts for Supervised Classification. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15387-7_16
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DOI: https://doi.org/10.1007/978-3-642-15387-7_16
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