Abstract
In this work we are interested in the problem of mining very large distributed databases. We propose a distributed data mining technique which produces a meta-classifier that is both predictive and descriptive. This meta-classifier is made of a set of classification rules, which can be refined then validated. The refinement step, proposes to remove from the meta-classifier rules that according to their confidence coefficient, computed by statistical means, would not have a good prediction capability when used with new objects. The validation step uses some samples to fine-tune rules in the rule set resulted from the refinement step. This paper deals especially with the validation process. Indeed, we propose two validation techniques: the first one is very simple and the second one uses a Galois lattice. A detailed description of these processes is presented in the paper, as well as the experimentation proving the viability of our approach.
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Aoun-Allah, M., Mineau, G. (2008). Rule Validation of a Meta-classifier Through a Galois (Concept) Lattice and Complementary Means. In: Yahia, S.B., Nguifo, E.M., Belohlavek, R. (eds) Concept Lattices and Their Applications. CLA 2006. Lecture Notes in Computer Science(), vol 4923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78921-5_8
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DOI: https://doi.org/10.1007/978-3-540-78921-5_8
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