Ensemble based systems in decision making

By Robi Polikar. 2006

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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).”
Hierarchical retinal blood vessel segmentation based on feature and ensemble learning (2015, background): “Ensemble classification is a process in which multiple classifiers are strategically generated and combined to solve the same problem [30].”
A Survey on Ensemble Learning for Data Stream Classification (2017, background): “One of the main goals that researchers pursue while designing an ensemble is to permit that each of the ensemble members be as unique as possible, particularly with respect to misclassiﬁcations [Polikar 2006].”
Voting based extreme learning machine (2012, methodology): “The main idea in V-ELM is to perform multiple independent ELM training instead of a single ELM training, and then make the final decision based on the majority voting method [29].”
Machine Learning in Enzyme Engineering (2019, methodology): “learning the optimal architecture of a predictor from the data (hyperparameter optimization) (92), smart aggregation of several predictions from multiple methods (93), and introducing robust confidence scores for predictions (94).”
Machine learning in concrete strength simulations: Multi-nation data analytics (2014, background): “Therefore, the strategy used in ensemble systems is to create many classifiers and combine their outputs such that the combination improves upon the performance of single classifiers in isolation [52].”
PR2 looking at things — Ensemble learning for unstructured information processing with Markov logic networks (2014, background): “Polikar [15] presents a thorough analysis of ensemble based systems, giving several reasons in favor of choosing them (e.g. statistical, lack or abundance of data, data fusion).”
A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery (2016, methodology): “Hybrid models refer to the integration of different models, either using various approaches depending on the task, for instance a DDM for diagnostics and a PbM for prognostics, or a combination of several models that represent the same phenomena to obtain a more robust health assessment, so-called e”
A hybrid intelligent system for medical data classification (2014, background): “From the perspective of decision making, we commonly seek a second opinion (and sometimes even more) before making important decisions, especially one that has medical implications (Polikar, 2006).”
Link Prediction in Complex Networks: A Survey (2010, methodology): “This so-called ensemble learning method can obtain better prediction performance than could be obtained from any of the individual algorithms [170].”
Challenges in discriminating profanity from hate speech (2017, methodology): “Various other methods exist and the interested reader can refer to the exposition by Polikar (2006).”
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling (2013, background): “In the specialized literature, there are several practical and theoretical reasons for the usage of ensemble approaches instead of non-ensemble ones (in depth discussions can be found in [33,41,46]).”
Network Anomaly Detection: Methods, Systems and Tools (2014, background): “1) Ensemble-based methods and systems: The idea behind the ensemble methodology is to weigh several individual classifiers, and combine them to obtain an overall classifier that outperforms every one of them [172]–[176].”
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, methodology): “Ensemble-based classifiers, also known as multiple classifier systems [101], try to improve the performance of single classifiers by inducing several classifiers and combining them to obtain a new classifier that outperforms every one of them.”
Robust Sentiment Detection on Twitter from Biased and Noisy Data (2010, background): “Therefore, the diversity of labelers is a key element in combining them (Polikar, 2006).”