Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability

The generation of predictive models is a frequent task in data mining with the objective of generating highly precise and interpretable models. The data reduction is an interesting preprocessing approach that can allow us to obtain predictive models with these characteristics in large size data sets. In this paper, we analyze the rule classification model based on decision trees using a training selected set via evolutionary stratified instance selection. This method faces the scaling problem that appears in the evaluation of large size data sets, and the trade off interpretability-precision of the generated models.