Semantic-Guided RL for Interpretable Feature Engineering

TL;DR

This paper introduces SMART, a hybrid semantic-guided reinforcement learning approach for automated feature engineering that enhances model accuracy and interpretability by leveraging knowledge graphs and description logics.

Contribution

The paper presents a novel method combining semantic reasoning and deep reinforcement learning to generate interpretable features automatically.

Findings

Significantly improves prediction accuracy on public datasets.

Ensures high interpretability of generated features.

Effectively combines semantic reasoning with reinforcement learning.

Abstract

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.