⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

January 1, 2026International Journal of Enterprise Network Management

A Relative Entropy-Based Feature Selection and Failure Prognosis using Ensemble Learning

Navin M Parthiban Palanisamy

View Full Paper

Ask AI

Bookmark

Share

Key Points

This research aims to develop a method for feature selection and failure prognosis using relative entropy and ensemble learning techniques.
Utilizes relative entropy for feature selection in data analysis.
Applies ensemble learning methods to predict failure events.
Implements machine learning algorithms for enhanced predictive accuracy.
Achieves improved accuracy in failure prognosis compared to traditional methods.
Demonstrates significant reduction in false positive rates.
Finds that relative entropy enhances feature relevance in predictive modeling.

Implication

Investigates failure prognosis using ensemble learning, suggesting effective feature 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

M et al. (2026) studied this question.

synapsesocial.com/papers/6a192e4efab5b468c4417647 https://doi.org/10.1504/ijenm.2026.10078810

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 software fault prediction model using driving training-inspired metaheuristic feature selection frameworkOriginal Research · 2026
2Enhance Belief Propagation Decoding using Ensemble Learning ApproachesOriginal Research · 2025
3Design and evaluation of a textbook difficulty prediction model based on multidimensional feature fusionsOriginal Research · 2026
4Financial failure prediction in central public sector enterprises using machine learning techniquesOriginal Research · 2026
5AI-driven ensemble learning for optimised cloud server performance and energy efficiencyOriginal Research · 2026