Bagged ensembles with tunable parameters

A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data

(2018) Guillem Collell et al.   NEUROCOMPUTING

Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data

(2017) Mateusz Lango et al.   JOURNAL OF INTELLIGENT INFORMATION SYSTEMS

A hybrid simulation modeling framework for regional food hubs

(2017) Anuj Mittal et al.   Journal of Simulation

A Multicriteria Weighted Vote-Based Classifier Ensemble for Heart Disease Prediction

(2015) Saba Bashir et al.   COMPUTATIONAL INTELLIGENCE

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

A weighted random forests approach to improve predictive performance

(2013) Stacey J. Winham et al.   Statistical Analysis and Data Mining

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

(2011) M. Galar et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE

Out-of-bag estimation of the optimal sample size in bagging

(2009) Gonzalo Martínez-Muñoz et al.   PATTERN RECOGNITION

Roughly balanced bagging for imbalanced data

(2009) Shohei Hido et al.   Statistical Analysis and Data Mining