  The quality of Machine Learning (ML) models strongly depends on the input
data, as such Feature Engineering (FE) is often required in ML. In addition,
with the proliferation of ML-powered systems, especially in critical contexts,
the need for interpretability and explainability becomes increasingly
important. Since manual FE is time-consuming and requires case specific
knowledge, we propose KRAFT, an AutoFE framework that leverages a knowledge
graph to guide the generation of interpretable features. Our hybrid AI approach
combines a neural generator to transform raw features through a series of
transformations and a knowledge-based reasoner to evaluate features
interpretability using Description Logics (DL). The generator is trained
through Deep Reinforcement Learning (DRL) to maximize the prediction accuracy
and the interpretability of the generated features. Extensive experiments on
real datasets demonstrate that KRAFT significantly improves accuracy while
ensuring a high level of interpretability.
