A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees

The main objective of discretization is to transform numerical attributes into discrete ones. The intention is to provide the possibility to use some learning algorithms which require discrete data as input and to help the experts to understand the data more easily. Due to the fact that in classification problems there are high interactions among multiple attributes, we propose the use of evolutionary algorithms to select a subset of cut points for multivariate discretization based on a wrapper fitness function. The algorithm proposed has been compared with the best state-of-the-art discretizers with two decision trees-based classifiers: C4.5 and PUBLIC. The results reported indicate that our proposal outperforms the rest of the discretizers in terms of accuracy and requiring a lower number of intervals.