用博弈论筛选关键特征，大幅降低训练成本。
Strategic inputs: feature selection from game-theoretic perspective
- 将特征视为玩家，通过协同效应评估重要性
- 实验显示计算量显著减少，性能几乎不变
- 适合处理大规模表格数据的高效建模
数据量的指数级增长导致机器学习模型训练的计算成本不断上升。然而，许多特征对模型性能无益却消耗大量计算资源。本文提出一种基于博弈论的端到端表格数据特征选择框架。将特征选择过程建模为合作博弈，特征作为参与者，其重要性通过协同作用与边际贡献评估确定。该框架包含四个核心组件：样本选择、博弈论特征重要性评估、冗余特征剔除和优化模型训练。实验结果表明，该方法在保持预测性能的同时实现显著的计算节省，为大规模机器学习的计算挑战提供高效解决方案。源代码可在 https://github.com/vectorsss/strategy_inputs 获取。
原文摘要 · Abstract (English)
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.
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