Two-Stage Feature Generation with Transformer and Reinforcement Learning
This addresses the labor-intensive and inefficient nature of traditional feature generation for machine learning practitioners, though it appears incremental as it builds on existing automated techniques.
The paper tackles the problem of automated feature generation by proposing a Two-Stage Feature Generation (TSFG) framework that integrates Transformer-based encoder-decoder architecture with Proximal Policy Optimization (PPO), resulting in outperforming existing state-of-the-art methods in feature quality and adaptability.
Feature generation is a critical step in machine learning, aiming to enhance model performance by capturing complex relationships within the data and generating meaningful new features. Traditional feature generation methods heavily rely on domain expertise and manual intervention, making the process labor-intensive and challenging to adapt to different scenarios. Although automated feature generation techniques address these issues to some extent, they often face challenges such as feature redundancy, inefficiency in feature space exploration, and limited adaptability to diverse datasets and tasks. To address these problems, we propose a Two-Stage Feature Generation (TSFG) framework, which integrates a Transformer-based encoder-decoder architecture with Proximal Policy Optimization (PPO). The encoder-decoder model in TSFG leverages the Transformer's self-attention mechanism to efficiently represent and transform features, capturing complex dependencies within the data. PPO further enhances TSFG by dynamically adjusting the feature generation strategy based on task-specific feedback, optimizing the process for improved performance and adaptability. TSFG dynamically generates high-quality feature sets, significantly improving the predictive performance of machine learning models. Experimental results demonstrate that TSFG outperforms existing state-of-the-art methods in terms of feature quality and adaptability.