FEST: New Machine Learning Method Combines Expert Knowledge with Automated Feature Engineering
Researchers introduced FEST (Feature Engineering with Self-evolving Trees), a machine learning method that automatically generates interpretable features from unstructured text and images while aligning with expert knowledge. The approach combines semantic and deterministic feature generation with tree-guided evolution, outperforming existing methods across brand classification, content moderation, and stress detection tasks. This work addresses a critical need in high-stakes domains like healthcare and content moderation where ML models must be transparent and aligned with professional standards.
FEST is a novel feature engineering system designed to bridge the gap between automated machine learning and expert domain knowledge in high-stakes applications. The method uses a dual-stream approach generating both semantic and deterministic features, applies semantic deduplication, and employs tree-guided iterative evolution to discover auditable features from raw text and images. In evaluation across 20 classifier-task combinations, FEST achieved superior performance with a mean improvement of 4.2 percentage points over baseline methods. The system demonstrated 60-80% coverage of expert-designed features at strict semantic-alignment thresholds, validated through both LLM-as-judge evaluation and human expert studies that rated generated features highly on relevance, clarity, and actionability. When seeded with expert guidelines, FEST improved accuracy by 6-12 percentage points on average. The researchers also released BrandGuide, a new dataset pairing expert-designed features with over 1 million assets across 2,683 brands to enable systematic evaluation of expert alignment in automated feature engineering.
What's missing
The study's limitations and scope constraints are not detailed in the abstract. Specific information about computational requirements, scalability to larger datasets, failure modes, and generalization to domains beyond brand compliance, clinical care, and content moderation would provide important context for practitioners considering adoption.
What different sources said
- arXiv cs.AICenter
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
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