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Communication Dans Un Congrès Année : 2010

Optimized Representation for Classifying Qualitative Data

Résumé

Extracting knowledge out of qualitative data is an ever-growing issue in our networking world. Opposite to the widespread trend consisting of extending general classification methods to zero/one-valued qualitative variables, we explore here another path: we first build a specific representation for these data, respectful of the non-occurrence as well as presence of an item, and making the interactions between variables explicit. Combinatorics considerations in our Midova expansion method limit the proliferation of itemsets when building level k+1 on level k, and limit the maximal level K. We validate our approach on three of the public access datasets of University of California, Irvine, repository: our generalization accuracy is equal or better than the best reported one, to our knowledge, on Breast Cancer and TicTacToe datasets, honorable on Monks-2 near-parity problem.
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Dates et versions

hal-00460307 , version 1 (26-02-2010)

Identifiants

  • HAL Id : hal-00460307 , version 1

Citer

Martine Cadot, Alain Lelu. Optimized Representation for Classifying Qualitative Data. Second International Conference on Advances in Databases, Knowledge, and Data Applications - DBKDA 2010, Apr 2010, Menuires, The Three Valleys, France. pp.241-246. ⟨hal-00460307⟩
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