Quantum Feature Selection Methods for Improved Machine Learning Models

Prasanth Sp, Yukti Varshney

Published Dec 7, 2024• 0 citations

Abstract

Feature selection was a critical process in machine learning that enhances model performance by identifying the most relevant features from high-dimensional datasets. This book chapter delves into various feature selection methodologies, emphasizing the significance of hybrid approaches that leverage the strengths of different techniques, including filter, wrapper, and embedded methods. It critically examines the advantages and limitations of each method, providing insights into their applicability across diverse domains. The chapter also explores future directions in hybrid feature selection, including the integration of advanced algorithms, adaptation to varying data types, and the incorporation of domain knowledge. Emphasizing computational efficiency and real-time application potential, this work serves as a comprehensive guide for researchers and practitioners aiming to enhance machine learning models through effective feature selection. The findings and discussions presented herein contribute to the ongoing discourse in the field and provide a roadmap for future research initiatives.

DOI: 10.71443/9788197933646-13

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