Fusion Methods for Unsupervised Learning Ensembles

Name: Fusion Methods for Unsupervised Learning Ensembles
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 9783642162046
Price: 465.24 PLN
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Autor: Bruno Baruque, Emilio Corchado

Język:

Oprawa: Twarda

Wydawca: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

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The application of a committee of experts or ensemble learning to artificial neural networks§that ap...

Informacje o książce

Autor

Bruno Baruque, Emilio Corchado

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2010

strony

141

EAN

9783642162046

ISBN

3642162045

Enbook ID

01656769

Wydawca

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Waga

380

Wymiary

164 x 246 x 11

Pełny opis

The application of a committee of experts or ensemble learning to artificial neural networks§that apply unsupervised learning techniques is widely considered to enhance the effectiveness§of such networks greatly.§This book examines the potential of the ensemble meta-algorithm by describing and testing a§technique based on the combination of ensembles and statistical PCA that is able to determine§the presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results.§Its central contribution concerns an algorithm for the ensemble fusion of topology-preserving§maps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topology preserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms.§The experimental results demonstrate that, in the majority of cases, the WeVoS algorithm§outperforms earlier map-fusion methods and the simpler versions of the algorithm with which§it is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems.

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