Strategic inputs: feature selection from game-theoretic perspective

作者：Chi Zhao, Jing Liu, Elena Parilina · 发表于：arXiv.org · 年份：2025 · DOI：10.48550/arXiv.2510.24982 · 被引用次数：1 · 研究领域：Computer Science

The exponential growth of data volumes has led to escalating computational costs in machine learning model training. However, many features fail to contribute positively to model performance while consuming substantial computational resources. This paper presents an end-to-end feature selection framework for tabular data based on game theory. We formulate feature selection procedure based on a cooperative game where features are modeled as players, and their importance is determined through the evaluation of synergistic interactions and marginal contributions. The proposed framework comprises four core components: sample selection, game-theoretic feature importance evaluation, redundant feature elimination, and optimized model training. Experimental results demonstrate that the proposed method achieves substantial computation reduction while preserving predictive performance, thereby offering an efficient solution of the computational challenges of large-scale machine learning. The source code is available at https://github.com/vectorsss/strategy_inputs.