  Before any publication, data analysis of high-energy physics experiments must
be validated. This validation is granted only if a perfect understanding of the
data and the analysis process is demonstrated. Therefore, physicists prefer
using transparent machine learning algorithms whose performances highly rely on
the suitability of the provided input features. To transform the feature space,
feature construction aims at automatically generating new relevant features.
Whereas most of previous works in this area perform the feature construction
prior to the model training, we propose here a general framework to embed a
feature construction technique adapted to the constraints of high-energy
physics in the induction of tree-based models. Experiments on two high-energy
physics datasets confirm that a significant gain is obtained on the
classification scores, while limiting the number of built features. Since the
features are built to be interpretable, the whole model is transparent and
readable.
