Random Generalized Additive Logistic Forest: A Novel Ensemble Method for Robust Binary Classification

Random Generalized Additive Logistic Forest: A Novel Ensemble Method for Robust Binary Classification

Ensemble methods have proven highly effective in enhancing predictive performance by combining multiple models. We introduce a novel ensemble approach, the Random Generalized Additive Logistic Forest (RGALF), which integrates generalized additive models (GAMs) within a random forest framework to imp...

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Bibliographic Details
Main Authors: Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani, Nada MohammedSaeed Alharbi, Asma Ahmad Alzahrani
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Mathematics
Subjects:
generalized additive model (GAM)
random forest (RF)
logistic regression (LR)
ensemble methods
binary classification
nonlinearity
Online Access:https://www.mdpi.com/2227-7390/13/7/1214
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