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Item Details
Title: MULTIPLE FUZZY CLASSIFICATION SYSTEMS
By: Rafal Scherer
Format: Hardback

List price: £129.99


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ISBN 10: 3642306039
ISBN 13: 9783642306037
Publisher: SPRINGER-VERLAG BERLIN AND HEIDELBERG GMBH & CO. KG
Pub. date: 27 June, 2012
Edition: 2012 ed.
Series: Studies in Fuzziness and Soft Computing 288
Pages: 132
Description: This book presents a novel approach for exploratory data analysis with ensembles of various neuro-fuzzy systems. It places emphasis on ensembles that can work on incomplete data, thanks to rough set theory.
Synopsis: Fuzzy classifiers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientific and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions common to statistical classification. Rough set theory is useful when data sets are incomplete. It defines 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 classification. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a finite set of learning models, usually weak learners. The present book discusses the three aforementioned fields - 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 classification ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory. .
Illustrations: XII, 132 p.
Publication: Germany
Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Returns: Returnable
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