Leveraging Knowlegde Graphs for Interpretable Feature Generation

TL;DR

KRAFT is an automated feature engineering framework that uses knowledge graphs and deep reinforcement learning to generate interpretable features, improving model accuracy and transparency in machine learning applications.

Contribution

This paper introduces KRAFT, a novel hybrid AI framework that combines neural generation and knowledge-based reasoning for automated, interpretable feature creation in ML.

Findings

KRAFT significantly improves prediction accuracy.

KRAFT ensures high interpretability of generated features.

Experiments validate effectiveness on real datasets.

Abstract

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.