Semantic-Guided RL for Interpretable Feature Engineering
<|reference_start|>Semantic-Guided RL for Interpretable Feature Engineering: The quality of Machine Learning (ML) models strongly depends on the input data, as such generating high-quality features is often required to improve the predictive accuracy. This process is referred to as Feature Engineering (FE). However, since manual feature engineering is time-consuming and requires case-by-case domain knowledge, Automated Feature Engineering (AutoFE) is crucial. A major challenge that remains is to generate interpretable features. To tackle this problem, we introduce SMART, a hybrid approach that uses semantic technologies to guide the generation of interpretable features through a two-step process: Exploitation and Exploration. The former uses Description Logics (DL) to reason on the semantics embedded in Knowledge Graphs (KG) to infer domain-specific features, while the latter exploits the knowledge graph to conduct a guided exploration of the search space through Deep Reinforcement Learning (DRL). Our experiments on public datasets demonstrate that SMART significantly improves prediction accuracy while ensuring a high level of interpretability.<|reference_end|>
@article{bouadi2024semantic-guided,
    title={Semantic-Guided RL for Interpretable Feature Engineering},
    author={Mohamed Bouadi, Arta Alavi, Salima Benbernou, Mourad Ouziri},
    journal={arXiv preprint arXiv:2410.02519},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02519},
    primaryClass={cs.LG}
}