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Multiple Fuzzy Classification Systems

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 da...

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Detalles Bibliográficos
Autor principal: Scherer, Rafał
Lenguaje:eng
Publicado: Springer 2012
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-642-30604-4
http://cds.cern.ch/record/1501850
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author Scherer, Rafał
author_facet Scherer, Rafał
author_sort Scherer, Rafał
collection CERN
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.
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spelling cern-15018502021-04-21T23:55:41Zdoi:10.1007/978-3-642-30604-4http://cds.cern.ch/record/1501850engScherer, RafałMultiple Fuzzy Classification SystemsEngineeringFuzzy 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.Springeroai:cds.cern.ch:15018502012
spellingShingle Engineering
Scherer, Rafał
Multiple Fuzzy Classification Systems
title Multiple Fuzzy Classification Systems
title_full Multiple Fuzzy Classification Systems
title_fullStr Multiple Fuzzy Classification Systems
title_full_unstemmed Multiple Fuzzy Classification Systems
title_short Multiple Fuzzy Classification Systems
title_sort multiple fuzzy classification systems
topic Engineering
url https://dx.doi.org/10.1007/978-3-642-30604-4
http://cds.cern.ch/record/1501850
work_keys_str_mv AT schererrafał multiplefuzzyclassificationsystems