Feature Selection and Noise Control in High-Dimensional Data: Tourism vs. Healthcare

Shengyu Gu

doi:10.63313/AJET.9063

Authors

Shengyu Gu School of Geography and Tourism, Huizhou University, Huizhou, China Author

DOI:

https://doi.org/10.63313/AJET.9063

Keywords:

High-dimensional data, feature selection, noise control, multi-source governance, decision efficiency, smart tourism, precision healthcare, data purification, decision usability

Abstract

This study proposes a unified governance-oriented framework for managing high-dimensional multi-source data through the integration of feature selection and noise control, with the explicit goal of improving decision efficiency rather than purely technical accuracy. Drawing on comparative evidence from smart tourism and healthcare systems, the research demonstrates that the main challenge of high-dimensional data lies not in domain-specific content but in shared governance structures, including multi-source integration, systematic noise generation, and decision pressure under uncertainty. Feature selection is conceptualized as an institutional information purification mechanism that filters variables according to relevance, interpretability, and actionability. Noise control is framed as a governance pipeline involving profiling, harmonization, cleaning, validation, and continuous monitoring. Together, these processes transform complex and heterogeneous datasets into reliable and governable decision inputs. The study shifts evaluation criteria from medical or predictive accuracy toward governance-oriented indicators such as latency, stability, interpretability, robustness, and actionability. Tourism is positioned as the methodological origin and practical field of governance-oriented data purification, while healthcare serves as a comparative application context. The findings establish feature selection and noise control as a transferable management science methodology for high-dimensional data governance and institutional decision-making.

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