A Non-parametric Semi-supervised Discretization Method
Résumé
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 semi-supervised method with the original supervised MODL approach is presented. We demonstrate that the semi-supervised approach is asymptotically equivalent to the supervised approach, improved with a post-optimization of the intervals bounds location.
Mots clés
Bayesian methods
Bonding
Classification algorithms
data mining
Discretization
Input variables
Iterative algorithms
learning (artificial intelligence)
maximum likelihood estimation
minimal optimized description length
Non-parametric
nonparametric semisupervised discretization
Optimization methods
pattern classification
predictive model
Predictive models
Semi-supervised
semisupervised classification
semisupervised learning
supervised MODL