Classifier selection for majority voting

By Dymitr Ruta, Bogdan Gabrys. Information Fusion, 2005

661 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/30568d9ad6c5773f0745f0412f0e0d8ecca18c87

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In Computer Vision and Pattern Recognition

Citations in the corpus

Using the RRT algorithm to optimize classification systems for handwritten digits and letters (2008, neutral): “This is supported by [11].”
Meta-learning for time series forecasting and forecast combination (2010, background): “Other diversity measures have mainly been discussed in the context of classiﬁcation tasks, for example in [27] and [18].”
Layered Ensemble Architecture for Time Series Forecasting (2016, background): “The construction of the ensemble is usually divided into two phases [45].”
Data-driven exemplar model selection (2014, methodology): “It is related to the general problem of classifier selection which has attracted a lot of interest [2, 3, 7, 19, 32] in the past decade.”
A survey of multiple classifier systems as hybrid systems (2014, methodology): “To deal with the high computational complexity of ensemble pruning, several hybrid approaches have been proposed such as heuristic techniques [72,73], evolutionary algorithms [74,75], reinforcement learning [76], and competitive cross-validation techniques [77].”
Fake News Detection Using Machine Learning Ensemble Methods (2020, methodology): “One such technique is using voting classiﬁers where the ﬁnal classiﬁ-cation depends on the major votes provided by all algorithms [32].”
ForEx++: A New Framework for Knowledge Discovery from Decision Forests (2017, background): “…selection algorithms that prunes a number of decision trees from a decision forest while retaining or increasing ensemble accuracy (Adnan and Islam (2016c), Lu et al. (2010), Margineantu and Dietterich (1997), Martínez-Muñoz et al. (2009), MartínezMuñoz and Suárez (2004), Ruta and Gabrys (2005)).”
Data-driven Soft Sensors in the process industry (2009, methodology): “The idea of ensemble methods was brought further in Ruta and Gabrys (2005) where approach to the selection of single predictors, ensembles and multi-level structres were studied.”
Locally Weighted Ensemble Clustering (2016, background): “Typically, there are two major directions in ensemble learning, that is, ensemble classifiers [41]–[43] and ensemble clustering [44].”
From dynamic classifier selection to dynamic ensemble selection (2008, background): “Intuitively, we can see that classifiers will have different behaviors based on different sample subsets.”