Multiple Fuzzy Classification Systems

Language: English

Published by Springer, Springer Jun 2012, 2012

3642306039 / 9783642306037

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This item is printed on demand - Print on Demand Titel. Neuware -Fuzzy classi¿ers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scienti¿c and business applications. Fuzzy classi¿ers use fuzzy rules and do not require assumptions common to statistical classi¿cation. Rough set theory is useful when data sets are incomplete. It de¿nes a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classi¿cation. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a ¿nite set of learning models, usually weak learners.The present book discusses the three aforementioned ¿elds ¿ fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed on the possibility to combine fuzzy rules from fuzzy systems being members of classi¿cation ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory. .Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 144 pp. Englisch.

Seller Inventory # 9783642306037

Title
Multiple Fuzzy Classification Systems
Author
Rafa¿ Scherer
Publisher
Springer, Springer Jun 2012
Publication year
2012
Condition
Neu
Binding
Buch
Language
English
ISBN 10
3642306039
ISBN 13
9783642306037
Item weight
389 grams
Dimensions
241x160x13 mm

buchversandmimpf2000

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