Retrieval-Augmented Feature Generation for Domain-Specific Classification

Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data. An effective way to improve feature generation is by expanding the current feature space using existing features and enriching the informational content. However, generating new, interpretable features in application fields often requires domain-specific knowledge about the existing features. This paper introduces a new method RAFG for generating reasonable and explainable features specific to domain classification tasks. To generate new features with interpretability in domain knowledge, we perform information retrieval on existing features to identify potential feature associations, and utilize these associations to generate meaningful features. Furthermore, we develop a Large Language Model (LLM)-based framework for feature generation with reasoning to verify and filter features during the generation process. Experiments across several datasets in medical, economic, and geographic domains show that our RAFG method produces high-quality, meaningful features and significantly improves classification performance compared with baseline methods.