⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

January 1, 2026International Journal of Grid and Utility Computing

Fuzzy forests for feature selection in high-dimensional survey data: an application to the 2020 US presidential election

Sreemanti Dey R. Michael Alvarez

View Full Paper

Ask AI

Bookmark

Share

Key Points

The aim is to explore fuzzy forests for selecting important features in high-dimensional survey data related to the 2020 presidential election.
Utilized a fuzzy forests algorithm for feature selection.
Applied the methodology to survey data from the 2020 US presidential election.
Analyzed the significance of various features influencing voter behavior.
Identified critical features that significantly impact election outcomes.
Demonstrated the effectiveness of fuzzy forests in high-dimensional settings.

Implication

Analysis identifies key features impacting the 2020 US presidential election, suggesting useful selection methods.

Abstract

No abstract yet. The journal hasn't published one we can show.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Dey et al. (2026) studied this question.

synapsesocial.com/papers/69d0aee0659487ece0fa4c2d https://doi.org/10.1504/ijguc.2026.10077383

View Full Paper

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1A fuzzy evaluation of AI-assisted English teaching quality based on random forest regression algorithmOriginal Research · 2026
2Artificial Intelligence in Fuzzy Optimisation and Decision Making: a Holistic Review of Current TrendsReview · 2026
3Pest Classification using Hybrid Deep Learning with Optimal Feature Selection TechniqueOriginal Research · 2026
4A Relative Entropy-Based Feature Selection and Failure Prognosis using Ensemble LearningOriginal Research · 2026
5Design of multi-sensor data deep fusion algorithm based on fuzzy setsOriginal Research · 2026