Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Decision-Oriented Rough Set Methods Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Rough Set Approximations in Multi-scale Interval Information Systems

verfasst von : Shen-Ming Gu, Ya-Hong Wan, Wei-Zhi Wu, Tong-Jun Li

Erschienen in: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

With the view point of granular computing, the notion of a granule may be interpreted as one of the numerous small particles forming a larger unit. There are different granules at different levels of scale in data sets having hierarchical structures. Human beings often observe objects or deal with data hierarchically structured at different levels of granulations. And in real-world applications, there may exist multiple types of data in interval information systems. Therefore, the concept of multi-scale interval information systems is first introduced in this paper. The lower and upper approximations in multi-scale interval information systems are then defined, and the accuracy and the roughness are also explored. Monotonic properties of these rough set approximations with different levels of granulations are analyzed with illustrative examples.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Empirical Risk Minimization for Variable Consistency Dominance-Based Rough Set Approach
Nächstes Kapitel A New Subsystem-Based Definition of Generalized Rough Set Model
Literatur
1.
Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Boston (2002)MATH
2.
Bargiela, A., Pedrycz, W.: Toward a theory of granular computing for human-centered information processing. IEEE Trans. Fuzzy Syst. 16, 320–330 (2008)CrossRef
3.
Bittner, T., Smith, B.: A theory of granular partitions. In: Duckham, M., Goodchild, M.F., Worboys, M.F. (eds.) Foundations of Geographic Information Science, pp. 117–151. Taylor & Francis, London (2003)CrossRef
4.
Cornelis, C., Jensen, R., Hurtado, G., Slezak, D.: Attribute selection with fuzzy decision reducts. Inf. Sci. 180, 209–224 (2010)MathSciNetCrossRefMATH
5.
Dai, J., Tian, H., Wang, W., Liu, L.: Decision rule mining using classification consistency rate. Knowl. Based Syst. 43, 95–102 (2013)CrossRef
6.
Gu, S.-M., Wu, W.-Z.: Knowledge acquisition in inconsistent multi-scale decision systems. In: Yao, J.T., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 669–678. Springer, Heidelberg (2011) CrossRef
7.
Gu, S.M., Wu, W.Z.: On knowledge acquisition in multi-scale decision systems. Int. J. Mach. Learn. Cybern. 4, 477–486 (2013)CrossRef
8.
Gu, S.M., Wu, W.Z., Zheng, Y.: Rule acquisition in consistent multi-scale decision systems. In: Proceedings of 8th International conference on Fuzzy System and Knowledge Discovery, pp. 390–393. IEEE Computer Society, Los Alamitos (2011)
9.
Gu, S.M., Li, X., Wu, W.Z., Nian, H.: Multi-granulation rough sets in multi-scale information systems. In: Proceedings of the 2013 International Conference on Machine Learning and Cybernetics, pp. 108–113, Tianjin (2013)
10.
Hu, Q.H., Liu, J.F., Yu, D.R.: Mixed feature selection based on granulation and approximation. Knowl. Based Syst. 21, 294–304 (2008)CrossRef
11.
Inuiguchi, M., Hirano, S., Tsumoto, S.: Rough Set Theory and Granular Computing. Springer, Heidelberg (2002)MATH
12.
Keet, C.M.: A formal theory of granularity. Ph.D. thesis, KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, Italy (2008)
13.
Leung, Y., Zhang, J.S., Xu, Z.B.: Clustering by scale-space filtering. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1396–1410 (2000)CrossRef
14.
Leung, Y., Fischer, M., Wu, W., Mi, J.: A rough set approach for the discovery of classification rules in interval-valued information systems. Int. J. Approx. Reason. 47, 233–246 (2008)MathSciNetCrossRefMATH
15.
Lin, T.Y., Yao, Y.Y., Zadeh, L.A.: Data Mining, Rough Sets and Granular Computing. Physica- Verlag, Heidelberg (2002) CrossRefMATH
16.
Ma, J.-M., Zhang, W., Wu, W.-Z., Li, T.-J.: Granular computing based on a generalized approximation space. In: Yao, J.T., Lingras, P., Wu, W.-Z., Szczuka, M.S., Cercone, N.J., Ślȩzak, D. (eds.) RSKT 2007. LNCS (LNAI), vol. 4481, pp. 93–100. Springer, Heidelberg (2007) CrossRef
17.
Mi, J.S., Wu, W.Z., Zhang, W.X.: Approaches to knowledge reduction based on variable precision rough setsmodel. Inf. Sci. 159, 255–272 (2004)CrossRefMATH
18.
19.
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston (1991)CrossRefMATH
20.
Qian, Y.H., Liang, J.Y., Dang, C.Y.: Knowledge structure, knowledge granulation and knowledge distance in a knowledge base. Int. J. Approx. Reason. 50, 174–188 (2009)MathSciNetCrossRefMATH
21.
22.
23.
Tsumoto, S.: Automated extraction of hierarchical decision rules from clinical databases using rough set model. Expert Syst. Appl. 24, 189–197 (2003)CrossRef
24.
Wu, W.-Z.: Rough set approximations based on granular labels. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS, vol. 5908, pp. 93–100. Springer, Heidelberg (2009) CrossRef
25.
Wu, W.Z., Leung, Y.: Theory and applications of granular labelled partitions in multi-scale decision tables. Inf. Sci. 181, 3878–3897 (2011)CrossRefMATH
26.
27.
Yao, Y.Y.: Stratified rough sets and granular computing. In: Dave, R.N., Sudkamp, T. (eds.) Proceedings of 18th International Conference of the North American Fuzzy Information Processing Society, pp. 800–804. IEEE Press, New York (1999)
28.
Yao, Y.Y.: Information granulation and rough set approximation. Int. J. Intell. Syst. 16, 87–104 (2001)CrossRefMATH
29.
Yao, Y.: A partition model of granular computing. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B., Swiniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 232–253. Springer, Heidelberg (2004)
30.
Yao, Y.Y.Y., Liau, C.-J., Zhong, N.: Granular computing based on rough sets, quotient space theory, and belief functions. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 152–159. Springer, Heidelberg (2003) CrossRef
31.
Yao, Y.Y., Li, X.: Comparison of rough-set and interval-set models for uncertain reasoning. Fundam. Inform. 27, 289–298 (1996)MathSciNetCrossRefMATH
32.
Zadeh, L.A.: Fuzzy sets and information granularity. In: Gupta, N., Ragade, R., Yager, R.R. (eds.) Advances in Fuzzy Set Theory and Applications, pp. 3–18. North-Holland, Amsterdam (1979)
33.
Zadeh, L.A.: Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90, 111–127 (1997)MathSciNetCrossRefMATH
Metadaten
Titel
Rough Set Approximations in Multi-scale Interval Information Systems
verfasst von
Shen-Ming Gu
Ya-Hong Wan
Wei-Zhi Wu
Tong-Jun Li
Copyright-Jahr
2015
Buch
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Print ISBN: 978-3-319-25782-2

Electronic ISBN: 978-3-319-25783-9

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-25783-9_7