GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement

• URL: http://arxiv.org/abs/2508.20824v1
• Date: Thu, 28 Aug 2025 14:21:08 GMT
• Title: GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement
• Authors: Yang Gao, Dongjie Wang, Scott Piersall, Ye Zhang, Liqiang Wang,
• Abstract summary: Feature transformation plays a critical role in enhancing machine learning model performance by optimizing data representations.<n>Recent state-of-the-art approaches address this task as a continuous embedding optimization problem, converting discrete search into a learnable process.<n>We propose a novel framework that accomplishes automated feature transformation through four steps.
• Score: 18.888674282162032
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Feature transformation plays a critical role in enhancing machine learning model performance by optimizing data representations. Recent state-of-the-art approaches address this task as a continuous embedding optimization problem, converting discrete search into a learnable process. Although effective, these methods often rely on sequential encoder-decoder structures that cause high computational costs and parameter requirements, limiting scalability and efficiency. To address these limitations, we propose a novel framework that accomplishes automated feature transformation through four steps: transformation records collection, embedding space construction with a revised Generative Pre-trained Transformer (GPT) model, gradient-ascent search, and autoregressive reconstruction. In our approach, the revised GPT model serves two primary functions: (a) feature transformation sequence reconstruction and (b) model performance estimation and enhancement for downstream tasks by constructing the embedding space. Such a multi-objective optimization framework reduces parameter size and accelerates transformation processes. Experimental results on benchmark datasets show that the proposed framework matches or exceeds baseline performance, with significant gains in computational efficiency. This work highlights the potential of transformer-based architectures for scalable, high-performance automated feature transformation.

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