Ensemble Machine Learning: Methods and Applications

By Cha Zhang, Yunqian Ma. 2012

877 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c0f7fcc2e03be4395625b6757b17b8834632b952

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Citations in the corpus

Tree-Based Conditional Portfolio Sorts: The Relation between Past and Future Stock Returns (2016, unknown): “Consider the 13For further reading on decision-trees, see Hastie et al. (2009), Zhang and Ma (2012), Murphy (2012), or Criminisi and Shotten (2013).”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, background): “The combination of different classifiers can provide results beyond the original set of hypotheses; thus, EL can adapt well to a problem [171].”
FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation (2022, background): “In addition, it also helps improve model robustness by reducing the spread or dispersion of the predictions (Zhang and Ma, 2012).”
Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. (2019, methodology): “Ensemble modeling is the strategy of simultaneously using a number of classifiers to improve the 270 accuracy and quality of prediction models (Dietterich, 2000; Zhang and Ma, 2012; Buchen and 271 Wohlrabe, 2011).”
A survey on missing data in machine learning (2021, methodology): “This has been the case in machine learning competitions, where the triumphant models used ensemble techniques [103].”
Reliable Data Distillation on Graph Convolutional Network (2020, methodology): “Ensemble Learning [13] is a class of machine learning techniques that combines the predictions of different models into a more accurate one.”