Name: Diversity Role in Designing Multiple Classifier Systems Using Matlab: Designing of Mcs: A Diversity Approach (en Inglés)
SKU: 52689101
Price: 67.28 USD
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Author: Muhammad Atta Othman Ahmed Khfagy

portada Diversity Role in Designing Multiple Classifier Systems Using Matlab: Designing of Mcs: A Diversity Approach (en Inglés)

Diversity Role in Designing Multiple Classifier Systems Using Matlab: Designing of Mcs: A Diversity Approach (en Inglés)

Formato

Libro Físico

Editorial

Lap Lambert Academic Publishing

Autor

Muhammad Atta Othman Ahmed Khfagy

Año

2020

Idioma

Inglés

N° páginas

104

Encuadernación

Tapa Blanda

ISBN13

9783659522406

Categorías

Tecnología de la información: cuestiones generales

Diversity Role in Designing Multiple Classifier Systems Using Matlab: Designing of Mcs: A Diversity Approach (en Inglés)

Muhammad Atta Othman Ahmed Khfagy (Autor) · Lap Lambert Academic Publishing · Tapa Blanda

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Reseña del libro "Diversity Role in Designing Multiple Classifier Systems Using Matlab: Designing of Mcs: A Diversity Approach (en Inglés)"

Multiple Classiﬁers Systems (MCS) perform in formation fusion of classiﬁcation decisions at different levels overcoming limitations of traditional approaches based on single classiﬁers. We address one of the main open issues about the use of Diversity in Multiple Classiﬁer Systems: the effectiveness of the explicit use of diversity measures for creation of classiﬁer ensembles. So far, diversity measures have been mostly used for ensemble pruning, namely, for selecting a subset of classiﬁers out of an original, larger ensemble. Here we focus on pruning techniques based on forward selection, since they allow a direct comparison with the simple estimation of accuracy of classiﬁer ensemble. We empirically carry out this comparison for several diversity measures and bench mark data sets, using bagging as the ensemble construction technique, and majority voting as the fusion rule.