Ensemble Methods: Foundations and Algorithms

By Zhiwei Zhou. 2012

298 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0748a4412d12ec2a4d198b034d1a6ebaaa26684f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Han-Jia Ye
2. Tao Qin
3. De-chuan Zhan
4. Dacheng Tao
5. Xu Tan
6. Da-Wei Zhou
7. Lijun Wu
8. Yoshua Bengio

Citations in the corpus

• Depth Growing for Neural Machine Translation (2019, methodology): “• Ensemble learning: What we propose in this paper is a single deeper model with hierarchical contextual information, although the deep-shallow decoding is similar to the ensemble methods in terms of inference complexity (Zhou, 2012).”
• Ensembles for feature selection: A review and future trends (2019, unknown): “…for researchers, that have investigated in many alternatives that have been proposed in classi(cid:12)cation, regression, preprocessing and other (cid:12)elds[89, 39, 90, 91, 92, 93], although there is not a clear and de(cid:12)nite winner method, as in many other areas of Machine Learning.”
• Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users (2020, background): “They can, therefore, be considered as a special case of ensemble learning [99], where instead of training one single model, a set of models is trained and their predictions are aggregated.”
• Financial credit risk assessment: a recent review (2015, background): “Many researchers have provided the supporting evidences that MCS methodology can produce better performance than individual credit risk assessment models (Deligianni and Kotsiantis 2012; Kuncheva 2004; Rokach 2010; Verikas et al. 2010; Zhou 2012).”
• BLCU_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention (2018, background): “Ensemble learning helps improve machine learning performance by combining several models (Zhou, 2012).”
• A Survey on Truth Discovery (2015, background): “In ensemble learning [54,82], the weights of base models are inferred through supervised training whereas truth discovery is unsupervised.”
• Binary relevance for multi-label learning: an overview (2018, background): “It is worth noting that stacking aggregation employs ensemble learning [52] to combine two sets of binary relevance models with deterministic label correlation exploitation, while ensemble learning can also be”
• Revisiting Meta-Learning as Supervised Learning (2020, methodology): “nimum adjustment. In our experiment, we adapt widely-used techniques for improving generalization abilities or facilitating optimization, including data augmentation, ensemble methods (Breiman, 1996; Zhou, 2012; Dietterich, 2000), joint training (e.g., multi-task learning (Argyriou et al., 2007)), a”
• Understanding Random Forests: From Theory to Practice (2014, background): “on that minimizes the average zero-one error with respect to the individual predictions. Alternatively, when individual models provide class probability estimates bp L ,m (Y = cjX = x), soft voting [Zhou, 2012 ] consists in averaging the class probability estimates and then predict the class which i”
• Sentiment classification: The contribution of ensemble learning (2014, methodology): “In Random Subspace, the base learners are constructed in random subspaces of the feature space [19,51].”
• Deep forest (2017, methodology): “Ensemble learning [63] is a machine learning paradigm where multiple learners (e.”
• An efficient instance selection algorithm for k nearest neighbor regression (2017, methodology): “AC CE PT ED M AN US CR IP T to achieve a good performance, while ensemble methods with multiple learning algorithms could obtain the better performance[28].”