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.

• Type: Conference
• Year: 2008
• Where: ICDM
• Authors: Alexis Bondu, Marc Boullé, Vincent Lemaire, Stéphane Loiseau, Béatrice Duval
• Keywords: Data Mining, ICDM 2008, Semi-supervised Classification Methods, Semi-supervised Discretization Method, Supervised Modl Approach