Ensemble Learning
Ensemble learning combines multiple machine learning models to improve prediction accuracy and robustness beyond the capabilities of individual models. Current research focuses on optimizing ensemble composition and diversity, exploring techniques like diversity-optimized pruning, span-level ensembling, and adaptive model selection to enhance performance while mitigating computational costs, particularly in resource-constrained environments. This approach is proving valuable across diverse applications, from healthcare (e.g., disease diagnosis, medication extraction) and natural language processing (e.g., text classification, question answering) to manufacturing (e.g., defect detection, productivity analysis) and beyond, offering improved accuracy and reliability in various prediction tasks.
Papers
SPINEX: Similarity-based Predictions and Explainable Neighbors Exploration for Regression and Classification Tasks in Machine Learning
M. Z. Naser, M. K. albashiti, A. Z. Naser
S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation
An Wang, Mengya Xu, Yang Zhang, Mobarakol Islam, Hongliang Ren
Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning
Geoffery Agorku, Divine Agbobli, Vuban Chowdhury, Kwadwo Amankwah-Nkyi, Adedolapo Ogungbire, Portia Ankamah Lartey, Armstrong Aboah
Uncertainty-Aware Vehicle Energy Efficiency Prediction using an Ensemble of Neural Networks
Jihed Khiari, Cristina Olaverri-Monreal