Zobrazit v EDS

Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

Uloženo v:
Podrobná bibliografie
Název: Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes
Autoři: Ahmad Al-Qerem, Ali Mohd Ali, Issam Jebreen, Ahmad Nabot, Mohammed Rajab, Mohammad Alauthman, Amjad Aldweesh, Faisal Aburub, Someah Alangari, Musab Alzgol
Zdroj: International Journal of Crowd Science, Vol 9, Iss 1, Pp 64-78 (2025)
Informace o vydavateli: Tsinghua University Press, 2025.
Rok vydání: 2025
Témata: data-driven decision-making, Technology, feature selection, predictive accuracy, socio-economic analysis, selection methods, TA1-2040, predictive models, Engineering (General). Civil engineering (General)
Popis: Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings.
Druh dokumentu: Article
ISSN: 2398-7294
DOI: 10.26599/ijcs.2023.9100035
Přístupová URL adresa: https://doaj.org/article/ef361240b5054fbbb78159006d9ffec3
Přístupové číslo: edsair.doi.dedup.....f4ce9450a8d92736efb33cd520967554
Databáze: OpenAIRE
Popis
Abstrakt:Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings.
ISSN:23987294
DOI:10.26599/ijcs.2023.9100035