Leveraging Knowlegde Graphs for Interpretable Feature Generation
This addresses the need for interpretable and efficient feature engineering in critical ML systems, though it is incremental as it builds on existing AutoFE methods.
The paper tackles the problem of automated feature engineering by proposing KRAFT, a framework that uses knowledge graphs to generate interpretable features, resulting in significant accuracy improvements on real datasets.
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.