Switching between selection and fusion in combining classifiers: an experiment

By Ludmila I. Kuncheva. IEEE Trans. Syst. Man Cybern. Part B, 2002

395 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7077281bb6251c542c5262961df0c6e514f9d6e0

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Mixture of experts: a literature survey (2012, neutral): “There also exist combination schemes lying between the two pure strategies (Kuncheva 2002).”
Ensemble based systems in decision making (2006, background): “Does the no-free-lunch theorem hold for combination rules as well? Many studies have been conducted to answer this question, including [18], [27], [28], [31], [36], [37], [42], [71], [72], among others.”
Dynamic selection of classifiers - A comprehensive review (2014, neutral): “[38] 5S (3,1,6) (3,0,7) NA (8,0,12)”
An up-to-date comparison of state-of-the-art classification algorithms (2017, background): “Kuncheva (2002) also explored the switching between selection and fusion in combining classiﬁers.”
The combining classifier: to train or not to train? (2002, background): “In the following section we will go into the issue o the choice and the size of the training set.”
From dynamic classifier selection to dynamic ensemble selection (2008, background): “Keywords: Oracle; Combining classifiers; Classifier selection; Ensemble selection; Pattern recognition; Majority voting; Ensemble of learning machines”
A Review of Ensemble Methods in Bioinformatics (2010, methodology): “Methods like base classifier selection and cascade-based classifiers are also widely used [19, 20].”
Layered Ensemble Architecture for Time Series Forecasting (2016, background): “An ensemble brings together several individual networks (base predictors) for improving the generalization performance of a learning system [10].”
A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification (2010, methodology): “[34] presented a combination of classifier selection and fusion by using statistical inference to switch between the two by applying classifier selection in those regions of the feature space where one classifier strongly dominates the others from the pool and fusion is applied in the remaining…”