Embedded Feature Construction in Fuzzy Decision Tree Induction for High Energy Physics Classification

Fuzzy decision trees have been successfully applied in numerous domains. The popularity of these models comes notably from their interpretability, namely the ability of humans to understand them. However, on the contrary to neural networks, the induction of such models does not include a generation of their own feature space. In this work, the embedding of feature construction in fuzzy decision tree induction algorithms is studied, so that they can create new input features, without affecting the overall interpretability of the model. This method is successfully applied to a classification problem in high-energy physics to study the benefits of having constructed features in fuzzy decision tree on the classification scores, allowing them to have their own interpretable representation of the data.