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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2012.
|
| Edición: | 1st ed. 2012. |
| Colección: | Studies in Fuzziness and Soft Computing,
288 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-642-30604-4 | ||
| 003 | DE-He213 | ||
| 005 | 20230713071935.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 120626s2012 gw | s |||| 0|eng d | ||
| 020 | |a 9783642306044 |9 978-3-642-30604-4 | ||
| 024 | 7 | |a 10.1007/978-3-642-30604-4 |2 doi | |
| 050 | 4 | |a Q342 | |
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| 072 | 7 | |a UYQ |2 thema | |
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| 100 | 1 | |a Scherer, Rafał. |e author. |0 (orcid)0000-0001-9592-262X |1 https://orcid.org/0000-0001-9592-262X |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Multiple Fuzzy Classification Systems |h [electronic resource] / |c by Rafał Scherer. |
| 250 | |a 1st ed. 2012. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2012. | |
| 300 | |a XII, 132 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 490 | 1 | |a Studies in Fuzziness and Soft Computing, |x 1860-0808 ; |v 288 | |
| 505 | 0 | |a Introduction to fuzzy systems -- Ensemble techniques -- Relational modular fuzzy systems -- Ensembles of the Mamdani fuzzy systems -- Logical type fuzzy systems -- Takagi-Sugeno fuzzy systems -- Rough-neuro-fuzzy Ensembles for Classification with Missing Data -- Concluding remarks and challenges for future research. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Computer simulation. | |
| 650 | 1 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Computer Modelling. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642306051 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642436574 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642306037 |
| 830 | 0 | |a Studies in Fuzziness and Soft Computing, |x 1860-0808 ; |v 288 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-30604-4 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
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| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||