Rough set theory ; a true landmark in data analysis
Along the years, rough set theory has earned a well-deserved reputation as a sound methodology for dealing with imperfect knowledge in a simple though mathematically sound way. This edited volume aims at continue stressing the benefits of applying rough sets in many real-life situations while still...
Salvato in:
| Altri autori: | , , , |
|---|---|
| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Edizione: | 1st ed. 2009. |
| Serie: | Studies in Computational Intelligence
174 |
| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Rough set theory, a true landmark in data analysis, Ajith Abraham, Rafael Falcón, Rafael Bello (eds.), Berlin, Springer, 2009, 1 vol. (XV-322 p.), Studies in computational intelligence, 978-3-540-89920-4 • Rough Set Theory: A True Landmark in Data Analysis, Texte imprimé, 9783540899228 • Rough set theory, a true landmark in data analysis, Ajith Abraham, Rafael Falcón, Rafael Bello (eds.), Berlin, Springer, 2009, 1 vol. (XV-322 p.), Studies in computational intelligence, 978-3-540-89920-4 • Rough set theory, a true landmark in data analysis, Ajith Abraham, Rafael Falcón, Rafael Bello (eds.), Berlin, Springer, 2009, 1 vol. (XV-322 p.), Studies in computational intelligence, 978-3-540-89920-4 |
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| 245 | 0 | 0 | |a Rough set theory ; a true landmark in data analysis |c edited by Janusz Kacprzyk, Ajith Abraham, Rafael Falcon, Rafael Bello. |
| 250 | |a 1st ed. 2009. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 174 |x 1860-9503 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a Theoretical Contributions to Rough Set Theory Rough Sets on Fuzzy Approximation Spaces and Intuitionistic Fuzzy Approximation Spaces Categorical Innovations for Rough Sets Granular Structures and Approximations in Rough Sets and Knowledge Spaces On Approximation of Classifications, Rough Equalities and Rough Equivalences Rough Set Data Mining Activities Rough Clustering with Partial Supervision A Generic Scheme for Generating Prediction Rules Using Rough Sets Rough Web Caching Software Defect Classification: A Comparative Study of Rough-Neuro-fuzzy Hybrid Approaches with Linear and Non-linear SVMs Rough Hybrid Models to Classification and Attribute Reduction Rough Sets and Evolutionary Computation to Solve the Feature Selection Problem Nature Inspired Population-Based Heuristics for Rough Set Reduction Developing a Knowledge-Based System Using Rough Set Theory and Genetic Algorithms for Substation Fault Diagnosis | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a Along the years, rough set theory has earned a well-deserved reputation as a sound methodology for dealing with imperfect knowledge in a simple though mathematically sound way. This edited volume aims at continue stressing the benefits of applying rough sets in many real-life situations while still keeping an eye on topological aspects of the theory as well as strengthening its linkage with other soft computing paradigms. The volume comprises 11 chapters and is organized into three parts. Part 1 deals with theoretical contributions while Parts 2 and 3 focus on several real world data mining applications. Chapters authored by pioneers were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. Academics, scientists as well as engineers working in the rough set, computational intelligence, soft computing and data mining research area will find the comprehensive coverage of this book invaluable | ||
| 700 | 1 | |a Kacprzyk, Janusz, |d 1947- |4 pbd | |
| 700 | 1 | |a Bello, Rafael, |d 19..- |4 pbd | |
| 700 | 1 | |a Abraham, Ajith, |d 1968- |4 pbd | |
| 700 | 1 | |a Falcón, Rafael, |d 1979- |4 pbd | |
| 776 | 0 | |0 153115939 |t Rough set theory |o a true landmark in data analysis |f Ajith Abraham, Rafael Falcón, Rafael Bello (eds.) |c Berlin |n Springer |d 2009 |p 1 vol. (XV-322 p.) |s Studies in computational intelligence |z 978-3-540-89920-4 | |
| 776 | 0 | |t Rough Set Theory: A True Landmark in Data Analysis |b Texte imprimé |z 9783540899228 | |
| 776 | 0 | |0 153115939 |t Rough set theory |o a true landmark in data analysis |f Ajith Abraham, Rafael Falcón, Rafael Bello (eds.) |c Berlin |n Springer |d 2009 |p 1 vol. (XV-322 p.) |s Studies in computational intelligence |z 978-3-540-89920-4 | |
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