Ensemble-based classifiers

By Lior Rokach. 2010

2,712 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “A well-known concept in ML is ensemble learning (Opitz & Maclin, 1999; Polikar, 2006; Rokach, 2010).”
Scalable and accurate deep learning with electronic health records (2018, methodology): “To optimize model accuracy, our final model is an ensemble of predictions from the three underlying model architectures [46].”
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “Model combination, also called ensemble method, typically aggregates together learning algorithms with significantly different nature and characteristics [124], [125], and seeks to”
Using Class Imbalance Learning for Software Defect Prediction (2013, methodology): “In addition to the aforementioned data-level and algorithm-level solutions, ensemble learning [22], [23] has become another major category of approaches to handling imbalanced data by combining multiple classifiers, such as SMOTEBoost [16], and AdaBoost.”
A new ensemble coevolution system for detecting HIV-1 protein coevolution (2015, methodology): “…two other ensemble strategies, namely majority voting (predictions supported by more than 50% of the considered methods) and Borda count (predictions made by all the methods) [28], both of which yet failed to outperform individual methods (average rankings beyond the top 10, data not shown).”
A Survey on Malware Detection Using Data Mining Techniques (2017, methodology): “For conclusion combination of the base classiﬁers, there are two main methods [Rokach 2010]: weighting methods (e.g., majority voting, Dempster-Shafer theory of evidence) and meta-learning methods (e.g., stacking).”
Boosting methods for multi-class imbalanced data classification: an experimental review (2020, background): “However, its main drawback is generating more classifiers, which increases computational complexity [35].”
A review: Deep learning for medical image segmentation using multi-modality fusion (2019, background): “The layer-level fusion network can effectively integrate and fully leverage multi-modal images [16] [15] [11] [57].”
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches (2012, background): “Ensembles [25], [26] are designed to increase the accuracy of a single classifier by training several different classifiers”
Towards SMS Spam Filtering: Results under a New Dataset (2013, methodology): “An ensemble classifier uses the mode of the outputs of several different classifiers to predict the class of a sample [44].”
Ensemble learning: A survey (2018, background): “Other extensive works (Polikar, 2006; Ren, Zhang, & Suganthan, 2016; Rokach, 2010) that provide a broad view of ensemble models fail to include several recently presented notable Received: 24 August 2017 Revised: 6 December 2017 Accepted: 23 January 2018 DOI: 10.1002/widm.1249 WIREs Data Mining…”
Random forests: from early developments to recent advancements (2014, methodology): “Ensemble learning is a machine learning paradigm where multiple models are used to solve the same problem (Kuncheva & Whitaker, 2003; Polikar, 2006; Rokach, 2010).”