Ensemble Strategy

Ensemble strategies in machine learning aim to improve model performance and robustness by combining predictions from multiple individual models. Current research focuses on optimizing ensemble selection and training methods, including the use of gradient boosting, Bayesian hyperparameter optimization, and reinforcement learning to guide the selection of diverse base models and efficient exploration of the model space. These advancements are impacting various fields, from improving forecasting accuracy in time series analysis to enhancing the reliability of explanations in complex predictive models and achieving better performance in deep learning applications.

Papers

January 31, 2024

Enhancing Score-Based Sampling Methods with Ensembles
Tobias Bischoff, Bryan Riel
Inverse Problem Diverse Ensemble Generative Diffusion Model Reverse Diffusion Ensemble Strategy

June 9, 2023

Consistent Explanations in the Face of Model Indeterminacy via Ensembling
Dan Ley, Leonard Tang, Matthew Nazari, Hongjin Lin, Suraj Srinivas, Himabindu Lakkaraju
Human Face Model Uncertainty Ensemble Method Model Ensembling Faithful Explanation Ensemble Strategy

April 8, 2023

A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching
Yangyang Guo, Hao Wang, Lei He, Witold Pedrycz, P. N. Suganthan, Yanjie Song
High Efficiency Positive Reinforcement Resume Job Team Formation Ensemble Strategy

April 4, 2023

Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks
Neelesh Mungoli
Deep Neural Network Ensemble Learning Feature Fusion Model Performance Adaptive Ensemble Ensemble Strategy

June 7, 2022

Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines
Çağatay Demirel, A. Aylin Tokuç, Ahmet Tezcan Tekin
Ensemble Model Prediction Performance Energy Pipeline Click Prediction Ensemble Strategy

March 7, 2022

Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion
Pieter Cawood, Terence van Zyl
Global Evaluation Meta Learning Ensemble Learning Art Specific Information Ensemble Forecast Model Fusion Exponential Smoothing E RNN Ensemble Strategy

January 1, 2022

AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection
Yunpu Zhao, Rui Zhang, Xiaqing Li
Classification Code Model Ensemble Ensemble Selection Ensemble Strategy AutoML Pipeline

Ensemble Strategy

Papers

Enhancing Score-Based Sampling Methods with Ensembles

Consistent Explanations in the Face of Model Indeterminacy via Ensembling

A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching

Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks

Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines

Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion

AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection