Výsledky vyhledávání - "Arnaiz-González Á"

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Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Tale

Autor: Kuncheva, Ludmila I., Matthews, Clare E., Arnaiz-González, Álvar, Rodríguez, Juan J.

In classification problems, the purpose of feature selection is to identify a small, highly discriminative subset of the original feature set. In many applications, the dataset may have thousands of features and only a few dozens of samples (sometime

Externí odkaz: http://arxiv.org/abs/2008.12025

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Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification

Autor: Kuncheva, Ludmila I., Arnaiz-González, Álvar, Díez-Pastor, José-Francisco, Gunn, Iain A. D.

A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. For two-class imbalanced problems, the classification success is typically measured by the geometric mean (

Externí odkaz: http://arxiv.org/abs/1804.07155

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