Skip to main content

A novel ensemble learning method using majority based voting of multiple selective decision trees

Author name : MOHAMMAD MOHIUDDIN AZAD
Publication Date : 2024-12-31
Journal Name : Computing

Abstract

Traditional decision tree algorithms are susceptible to bias when certain classes dominate the dataset and prone to overfitting, particularly if they are not pruned. Previous studies have shown that combining several models can mitigate these issues by improving predictive accuracy and robustness. In this study, we propose a novel approach to address these challenges by constructing multiple selective decision trees using the entirety of the input dataset and employing a majority voting scheme for output forecasting. Our method outperforms competing algorithms, including KNN, Decision Trees, Random Forest, Bagging, XGB, Gradient Boost, and ExtraTrees, achieving superior accuracy in five out of ten datasets. This practical exploration highlights the effectiveness of our approach in enhancing decision tree performance across diverse datasets.

Keywords

Decision tree · Multiple tree · Ensembles · Majority vote · Machine learning

Publication Link

https://doi.org/10.1007/s00607-024-01394-8

Block_researches_list_suggestions

Suggestions to read

HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things
Ahlem . Harchy Ep Berguiga
Generalized first approximation Matsumoto metric
AMR SOLIMAN MAHMOUD HASSAN
Structure–Performance Relationship of Novel Azo-Salicylaldehyde Disperse Dyes: Dyeing Optimization and Theoretical Insights
EBTSAM KHALEFAH H ALENEZY
“Synthesis and Characterization of SnO₂/α-Fe₂O₃, In₂O₃/α-Fe₂O₃, and ZnO/α-Fe₂O₃ Thin Films: Photocatalytic and Antibacterial Applications”
Asma Arfaoui
Contact