Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An introduction to variable and feature selection
The Journal of Machine Learning Research
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Supervised evaluation of Voronoi partitions
Intelligent Data Analysis
A grouping method for categorical attributes having very large number of values
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
| Hi-index | 0.00 |
In the field of data mining, data preparation has more and more in common with a bottleneck. Indeed, collecting and storing data becomes cheaper while modelling costs remain unchanged. As a result, feature selection is now usually performed. In the data preparation step, selection often relies on feature ranking. In the supervised classification context, ranking is based on the information that the explanatory feature brings on the target categorical attribute. With the increasing presence in the database of feature measured over time, i.e. dynamic features, new supervised ranking methods have to be designed. In this paper, we propose a new method to evaluate dynamic features, which is derived from a probabilistic criterion. The criterion is non-parametric and handles automatically the problem of overfitting the data. The resulting evaluation produces reliable results. Furthermore, the design of the criterion relies on an understandable and simple approach. This allows to provide meaningful visualization of the evaluation, in addition to the computed score. The advantages of the new method are illustrated on a telecommunication dataset.