基于量子自适应的机器学习的特征选择

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to feature selection based on quantum self-adaptation, aiming to enhance model performance and efficiency by dynamically adjusting feature importance. Traditional feature selection methods rely heavily on manual design, hindering automation and potentially suboptimal results. This work leverages quantum self-adaptation to iteratively refine feature importance scores, ultimately providing a more intelligent and adaptable feature selection strategy. We introduce a quantum-enhanced feature selection algorithm that dynamically adjusts feature weights based on data characteristics, achieving superior performance across a range of benchmark datasets. This approach offers a promising alternative to conventional methods, potentially leading to improved model accuracy and reduced computational cost.

DOI: 10.5281/zenodo.22203354

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