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2008
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A Non-parametric Semi-supervised Discretization Method

10 years 5 months ago
A Non-parametric Semi-supervised Discretization Method
Semi-supervised classification methods aim to exploit labelled and unlabelled examples to train a predictive model. Most of these approaches make assumptions on the distribution of classes. This article first proposes a new semi-supervised discretization method which adopts very low informative prior on data. This method discretizes the numerical domain of a continuous input variable, while keeping the information relative to the prediction of classes. Then, an in-depth comparison of this semisupervised method with the original supervised MODL approach is presented. We demonstrate that the semisupervised approach is asymptotically equivalent to the supervised approach, improved with a post-optimization of the intervals bounds location.
Alexis Bondu, Marc Boullé, Vincent Lemaire,
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICDM
Authors Alexis Bondu, Marc Boullé, Vincent Lemaire, Stéphane Loiseau, Béatrice Duval
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