Strategic inputs: feature selection from game-theoretic perspective

TL;DR

This paper introduces a game-theoretic feature selection framework for tabular data that reduces computational costs while maintaining model accuracy, addressing the challenge of large-scale data in machine learning.

Contribution

It proposes a novel end-to-end feature selection method based on cooperative game theory, modeling features as players to evaluate their importance and eliminate redundancies.

Findings

Significant reduction in computational costs

Preservation of predictive performance

Effective elimination of redundant features

Abstract

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.