Towards an interpretable Rules Ensemble algorithm for classification in a categorical data space

With the rapid growth of big data technology, classification plays an increasingly important role in decision making in many research areas. Several studies have been made in recent years to improve the accuracy-interpretability of classification models. In this paper, we present and discuss different classification methods, Random Forest, Boosting, CBA (Classification Based on Association) and Rulefit. We discuss the advantages and the limitations of each algorithm and finally we introduce a prototype model that combines some advantages that characterize the presented algorithms.