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- Random_subspace_method abstract "Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers each operating in a subspace of the original feature space, and outputs the class based on the outputs of these individual classifiers. Random subspace method has been used for decision trees (random decision forests), linear classifiers, support vector machines, nearest neighbours and other types of classifiers. This method is also applicable to one-class classifiers.The algorithm is an attractive choice for classification problems where the number of features is much larger than the number of training objects, such as fMRI data or gene expression data.In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for three reasons: simplification of models to make them easier to interpret by researchers/users,[1] shorter training times, enhanced generalization by reducing overfitting[2](formally, reduction of variance[1])The central premise when using a feature selection technique is that the data contains many features that are either redundant or irrelevant, and can thus be removed without incurring much loss of information.[2] Redundant or irrelevant features are two distinct notions, since one relevant feature may be redundant in the presence of another relevant feature with which it is strongly correlated.[3]".
- Random_subspace_method wikiPageID "30034676".
- Random_subspace_method wikiPageLength "4825".
- Random_subspace_method wikiPageOutDegree "6".
- Random_subspace_method wikiPageRevisionID "704766354".
- Random_subspace_method wikiPageWikiLink Category:Classification_algorithms.
- Random_subspace_method wikiPageWikiLink Category:Ensemble_learning.
- Random_subspace_method wikiPageWikiLink Ensemble_learning.
- Random_subspace_method wikiPageWikiLink One-class_classification.
- Random_subspace_method wikiPageWikiLink Statistical_classification.
- Random_subspace_method wikiPageWikiLink Support_vector_machine.
- Random_subspace_method wikiPageWikiLinkText "Random subspace method".
- Random_subspace_method wikiPageWikiLinkText "feature bagging".
- Random_subspace_method wikiPageWikiLinkText "random subset of the features".
- Random_subspace_method wikiPageWikiLinkText "random subspace method".
- Random_subspace_method wikiPageUsesTemplate Template:Reflist.
- Random_subspace_method subject Category:Classification_algorithms.
- Random_subspace_method subject Category:Ensemble_learning.
- Random_subspace_method hypernym Classifier.
- Random_subspace_method type Algorithm.
- Random_subspace_method comment "Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers each operating in a subspace of the original feature space, and outputs the class based on the outputs of these individual classifiers. Random subspace method has been used for decision trees (random decision forests), linear classifiers, support vector machines, nearest neighbours and other types of classifiers.".
- Random_subspace_method label "Random subspace method".
- Random_subspace_method sameAs Q7291993.
- Random_subspace_method sameAs m.0g57nrr.
- Random_subspace_method sameAs Q7291993.
- Random_subspace_method wasDerivedFrom Random_subspace_method?oldid=704766354.
- Random_subspace_method isPrimaryTopicOf Random_subspace_method.