Name: Dimensionality Reduction Through Classifier Ensembles (en Inglés)
Price: 28.69 USD
Availability: InStock
Author: Nasa Technical Reports Server (Ntrs)
ISBN: 9781287281108

portada Dimensionality Reduction Through Classifier Ensembles (en Inglés)

Dimensionality Reduction Through Classifier Ensembles (en Inglés)

Formato

Libro Físico

Autor

Nasa Technical Reports Server (Ntrs)

Editorial

Bibliogov

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

24.6 x 18.9 x 0.2 cm

Peso

0.07 kg.

ISBN13

9781287281108

Categorías

Política y gobierno

Dimensionality Reduction Through Classifier Ensembles (en Inglés)

Nasa Technical Reports Server (Ntrs) (Autor) · Bibliogov · Tapa Blanda

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Reseña del libro "Dimensionality Reduction Through Classifier Ensembles (en Inglés)"

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.