Multiple Fuzzy Classification Systems

By:

Rafal Scherer

Format:

Paperback / softback

Series:

Studies in Fuzziness and Soft Computing

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Description

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. .

Release date Australia

July 18th, 2014

Author

Rafal Scherer

Audience

Professional & Vocational

Illustrations

XII, 132 p.

Pages

132

Series

Studies in Fuzziness and Soft Computing

Dimensions

155x235x8

ISBN-13

9783642436574

Product ID

22639983

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Multiple Fuzzy Classification Systems

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