Ensemble Learning
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Benchmarks
Most implemented
Predicting the direction of stock market prices using random forest
Masksembles for Uncertainty Estimation
Gossip Learning with Linear Models on Fully Distributed Data
Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
Papers
Precision in Rice Variety Classification using Stacking-Based Ensemble Learning
Rice, a staple food for a significant portion of the global population, exhibits remarkable diversity in its varieties, presenting substantial challenges for accurate identification by consumers, traders, and farmers. Th…
Ensemble LearningTechnical Comparative Benchmarking Study: Advanced AI Hybrid Methods for Renewable Energy Farm Optimization and Forecasting
This study provides a comprehensive benchmarking of conventional machine learning (ML), ensemble learning, deep neural networks, recurrent architectures, Transformers, graph based models, and hybrid ensemble deep learnin…
Representation LearningEnsemble LearningHoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning
Ensembles of decision trees are well-established methods for data stream classification. In ensemble learning, Hoeffding Trees are widely adopted as base learners, performing periodic split attempts according to the Hoef…
Ensemble LearningChange DetectionHypEMBER: Hypernetwork-based Ensemble for Robust Policy Learning of Parametrized Dynamical Systems
In this work we investigate reinforcement learning (RL) as a framework for the robust control of parametrized dynamical systems in presence of measurements and model uncertainties. High-dimensional state spaces, expensiv…
Reinforcement LearningEnsemble LearningTriple-Phase Multimodal Knowledge Aggregation Framework for Microbial Keratitis Subtype Diagnosis on Slit-Lamp Photography
Microbial keratitis requires rapid pathogen identification to guide treatment, but culture- and PCR-based diagnostics are slow and resource-intensive. We developed a triple-phase multimodal framework for bacterial-versus…
Contrastive LearningEnsemble LearningSpectroscopy Analysis with Machine Learning Regression for the Quantification of Carbon and Nitrogen Contents in Inceptisol and Oxisol Soil Types: Comparing Different Preprocessing and Validation methods as well as Feature Importance
Near-Infrared (NIR) spectroscopy has emerged as a promising alternative to traditional soil analysis methods, offering advantages such as speed, low cost, and non-destructive testing. This work proposes a machine learnin…
Feature ImportanceEnsemble Learning