Name: Dimensionality Reduction Through Classifier Ensembles
Price: 956.00 TWD
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Author: Nasa Technical Reports Server (Ntrs)
ISBN: 9781287281108

portada Dimensionality Reduction Through Classifier Ensembles

Dimensionality Reduction Through Classifier Ensembles

Type

Physical Book

Author

Nasa Technical Reports Server (Ntrs)

Publisher

Bibliogov

Language

English

Pages

Format

Paperback

Dimensions

24.6 x 18.9 x 0.2 cm

Weight

0.07 kg.

ISBN13

9781287281108

Synopsis "Dimensionality Reduction Through Classifier Ensembles"

In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.

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Dimensionality Reduction Through Classifier Ensembles - Nasa Technical Reports Server (Ntrs)

Synopsis "Dimensionality Reduction Through Classifier Ensembles"

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