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2005
It is possible to reduce the error rate of a single classifier using a classifier ensemble. However, any gain in performance is undermined by the increased computation of performing classification several times. Here the AdaboostFS algorithm is proposed which builds on two popular areas of ensemble research: Adaboost and Ensemble Feature Selection (EFS)
D. B. Redpath, Katia Lebart
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It is possible to reduce the error rate of a single classifier using a classifier ensemble. However, any gain in performance is undermined by the increased computation of performing classification several times. Here the AdaboostFS algorithm is proposed which builds on two popular areas of ensemble research: Adaboost and Ensemble Feature Selection (EFS)
D. B. Redpath, Katia Lebart
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J. Mach. Learn. Res., 2006
Summary: In streamwise feature selection, new features are sequentially considered for addition to a predictive model. When the space of potential features is large, streamwise feature selection offers many advantages over traditional feature selection methods, which assume that all features are known in advance.
Zhou, Jing +3 more
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Summary: In streamwise feature selection, new features are sequentially considered for addition to a predictive model. When the space of potential features is large, streamwise feature selection offers many advantages over traditional feature selection methods, which assume that all features are known in advance.
Zhou, Jing +3 more
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Ensemble Feature Selection for Rankings of Features
2015In the last few years, ensemble learning has been the focus of much attention mainly in classification tasks, based on the assumption that combining the output of multiple experts is better than the output of any single expert. This idea of ensemble learning can be adapted for feature selection, in which different feature selection algorithms act as ...
Borja Seijo-Pardo +3 more
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Combining Feature Subsets in Feature Selection
2005In feature selection, a part of the features is chosen as a new feature subset, while the rest of the features is ignored. The neglected features still, however, may contain useful information for discriminating the data classes. To make use of this information, the combined classifier approach can be used.
Marina Skurichina, Robert P. W. Duin
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Feature Grouping-based Feature Selection
2017Feature selection (FS) is a process which aims to select input domain features that are most informative for a given outcome. Unlike other dimensionality reduction techniques, feature selection methods preserve the underlying semantics or meaning of the original data following reduction.
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A comprehensive survey on recent metaheuristics for feature selection
Neurocomputing, 2022Tansel Dokeroglu +2 more
exaly
Incremental feature selection by sample selection and feature-based accelerator
Applied Soft Computing, 2022Yanyan Yang 0001 +4 more
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