Quantum-Based Adaptive Feature Selection

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel framework for adaptive feature selection based on quantum-based features, designed to enhance model performance and efficiency, particularly in high-dimensional and complex data distributions. Traditional feature selection methods rely on manual design, limiting their adaptability to diverse data characteristics. Our proposed approach leverages the unique properties of quantum-like representations to automatically identify and select the most relevant features, fostering greater model accuracy and robustness. The core mechanism centers around a self-adaptive learning strategy that enables the algorithm to respond effectively to evolving data patterns. The study demonstrates the effectiveness of this framework through rigorous analysis and comparisons with existing methods.

DOI: 10.5281/zenodo.22201567

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