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
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 5/2020

04.05.2019 | Original Article

A comparison study of optimal scale combination selection in generalized multi-scale decision tables

verfasst von: Wei-Zhi Wu, Yee Leung

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2020

Einloggen

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

search-config
loading …

Abstract

Traditional rough set approach is mainly used to unravel rules from a decision table in which objects can possess a unique attribute-value. In a real world data set, for the same attribute objects are usually measured at different scales. The main objective of this paper is to study optimal scale combinations in generalized multi-scale decision tables. A generalized multi-scale information table is an attribute-value system in which different attributes are measured at different levels of scales. With the aim of investigating knowledge representation and knowledge acquisition in inconsistent generalized multi-scale decision tables, we first introduce the notion of scale combinations in a generalized multi-scale information table. We then formulate information granules with different scale combinations in multi-scale information systems and discuss their relationships. Furthermore, we define lower and upper approximations of sets with different scale combinations and examine their properties. Finally, we examine optimal scale combinations in inconsistent generalized multi-scale decision tables. We clarify relationships among different concepts of optimal scale combinations in inconsistent generalized multi-scale decision tables.
Vorheriger Artikel Tri-level thinking: models of three-way decision
Nächster Artikel Complex network analysis of three-way decision researches

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
Weitere Produktempfehlungen anzeigen
Literatur
1.
Bargiela A, Pedrycz W (2002) Granular computing: an introduction. Kluwer Academic Publishers, BostonMATH
2.
Bargiela A, Pedrycz W (2008) Toward a theory of granular computing for human-centered information processing. IEEE Trans Fuzzy Syst 16:320–330
3.
Greco S, Matarazzo B, Slowinski R (2002) Rough approximation by dominance relation. Int J Intell Syst 17:153–171MATH
4.
Grzymala-Busse JW, Zuo X (1998) Classification strategies using certain and possible rules. In: Polkowski L, Skowron A(Eds.), Rough Sets and Current Trends in Computing. Lecture Notes in Computer Science 1424: 37–44
5.
Gu SM, Wu WZ (2013) On knowledge acquisition in multi-scale decision systems. Int J Mach Learn Cyb 4:477–486
6.
Hao C, Li JH, Fan M, Liu WQ, Tsang ECC (2017) Optimal scale selection in dynamic multi-scale decision tables based on sequential three-way decisions. Inf Sci 415:213–232
7.
Hong TP, Tseng LH, Wang SL (2002) Learning rules from incomplete training examples by rough sets. Expert Syst Appl 22:285–293
8.
Inuiguchi M, Hirano S, Tsumoto S (2002) Rough set theory and granular computing. Springer, BerlinMATH
9.
Komorowski J, Pawlak Z, Polkowski L, Skowron A (1999) Rough sets: tutorial. In: Pal SK, Skowron A (eds) Rough fuzzy hybridization, a new trend in decision making. Springer, Berlin, pp 3–98
10.
Leung Y, Li DY (2003) Maximal consistent block technique for rule acquisition in incomplete information systems. Inf Sci 153:85–106MathSciNetMATH
11.
Leung Y, Wu WZ, Zhang WX (2006) Knowledge acquisition in incomplete information systems: a rough set approach. Eur J Operat Res 168:164–180MathSciNetMATH
12.
Leung Y, Zhang JS, Xu ZB (2000) Clustering by scale-space filtering. IEEE Trans Pattern Anal Mach Intell 22:1396–1410
13.
Li F, Hu BQ (2017) A new approach of optimal scale selection to multi-scale decision tables. Inf Sci 381:193–208
14.
Li F, Hu BQ, Wang J (2017) Stepwise optimal scale selection for multi-scale decision tables via attribute significance. Knowl Based Syst 129:4–16
15.
Li JH, Ren Y, Mei CL, Qian YH, Yang XB (2016) A comparative study of multigranulation rough sets and concept lattices via rule acquisition. Knowl Based Syst 91:152–164
16.
Lin TY (1997) Granular computing: From rough sets and neighborhood systems to information granulation and computing with words. In: European congress on intelligent techniques and soft computing, September 8-12: 1602–1606
17.
Lin TY, Yao YY, Zadeh LA (2002) Data mining, rough sets and granular computing. Physica-Verlag, HeidelbergMATH
18.
Lingras PJ, Yao YY (1998) Data mining using extensions of the rough set model. J Am Soc Inf Sci 49:415–422
19.
Luo C, Li TR, Chen HM, Fujita H, Yi Z (2018) Incremental rough set approach for hierarchical multicriteria classification. Inf Sci 429:72–87MathSciNet
20.
Luo C, Li TR, Huang YY, Fujita H (2019) Updating three-way decisions in incomplete multi-scale information systems. Inf Sci 476:274–289
21.
Ma JM, Zhang WX, Leung Y, Song XX (2007) Granular computing and dual Galois connection. Inf Sci 177:5365–5377MathSciNetMATH
22.
Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, BostonMATH
23.
Pawlak Z (2001) Drawing conclusions from data–the rough set way. Int J Intell Syst 16:3–11MATH
24.
Pedrycz W (2001) Granular computing: an emerging paradigm. Physica-Verlag, HeidelbergMATH
25.
Pedrycz W, Skowron A, Kreinovich V (2008) Handbook of granular computing. Wiley, New York
26.
Polkowski L, Tsumoto S, Lin TY (2000) Rough set methods and applications. Physica-Verlag, Berlin
27.
Shafer G (1976) A mathematical theory of evidence. Princeton University Press, PrincetonMATH
28.
She YH, Li JH, Yang HL (2015) A local approach to rule induction in multi-scale decision tables. Knowl Based Syst 89:398–410
29.
Skowron A (1989) The relationship between rough set theory and evidence theory. Bull Pol Acad Sci 37:87–90
30.
31.
Skowron A, Grzymala-Busse J (1994) From rough set theory to evidence theory. In: Yager RR, Fedrizzi M, Kacprzyk J (eds) Advance in the Dempster-Shafer theory of evidence. Wiley, New York, pp 193–236
32.
Skowron A, Stepaniuk J (2001) Information granules: towards foundations of granular computing. Int J Intell Syst 16:57–85MATH
33.
Skowron A, Stepaniuk J, Swiniarski RW (2012) Modeling rough granular computing based on approximation spaces. Inf Sci 184:20–43MATH
34.
Wu WZ, Chen CJ, Li TJ, Xu YH (2016) Comparative study on optimal granularities in inconsistent multi-granular labeled decision systems. Pat Recogn Artif Intell 29(12):1103–1111
35.
Wu WZ, Leung Y (2011) Theory and applications of granular labelled partitions in multi-scale decision tables. Inf Sci 181:3878–3897MATH
36.
Wu WZ, Leung Y (2013) Optimal scale selection for multi-scale decision tables. Int J Approx Reason 54:1107–1129MathSciNetMATH
37.
Wu WZ, Leung Y, Mi JS (2009) Granular computing and knowledge reduction in formal contexts. IEEE Trans Knowl Data Eng 21:1461–1474
38.
Wu WZ, Leung Y, Zhang WX (2002) Connections between rough set theory and Dempster-Shafer theory of evidence. Int J General Syst 31:405–430MathSciNetMATH
39.
Wu WZ, Qian YH, Li TJ, Gu SM (2017) On rule acquisition in incomplete multi-scale decision tables. Inf Sci 378:282–302MathSciNetMATH
40.
Wu WZ, Zhang M, Li HZ, Mi JS (2005) Knowledge reduction in random information systems via Dempster-Shafer theory of evidence. Inf Sci 174:143–164MathSciNetMATH
41.
Xie JP, Yang MH, Li JH, Zheng Z (2018) Rule acquisition and optimal scale selection in multi-scale formal decision contexts and their applications to smart city. Future Gener Comput Syst 83:564–581
42.
Xu YH, Wu WZ, Tan AH (2017) Optimal scale selections in consistent generalized multi-scale decision tables. In: Proceedings of international joint conference on rough sets, July 3-7, 2017, Olsztyn, Poland. Lecture Notes in Artificial Intelligence. Springer, Berlin, 10313: 185–198
43.
Yager RR (2008) Intelligent social network analysis using granular computing. Int J Intell Syst 23:1196–1219MATH
44.
Yao JT (2008) Recent developments in granular computing: A bibliometrics study. In: Proceedings of IEEE International Conference on Granular Computing, Hangzhou, China, Aug 26–28, 2008: 74–79
45.
Yao YY (2001) Information granulation and rough set approximation. Int J Intell Syst 16:87–104MATH
46.
Yao YY (2004) A partition model of granular computing, transactions on rough sets I. Lect Notes Comput Sci 3100:232–253
47.
Yao YY, Liau CJ, Zhong N (2003) Granular computing based on rough sets, quotient space theory, and belief functions. In: Proceedings of the 14th international symposium on foundations of intelligent systems. Lecture Notes in Computer Science, Springer, Berlin, 2871: 152–159
48.
Yao YY, Lingras PJ (1998) Interpretations of belief functions in the theory of rough sets. Inf Sci 104:81–106MathSciNetMATH
49.
Zadeh LA (1997) Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90:111–127MathSciNetMATH
50.
Zhang YQ (2005) Constructive granular systems with universal approximation and fast knowledge discovery. IEEE Trans Fuzzy Syst 13:48–57
51.
Zhang WX, Leung Y, Wu WZ (2003) Information systems and knowledge discovery (in Chinese). Science Press, Beijing
Metadaten
Titel
A comparison study of optimal scale combination selection in generalized multi-scale decision tables
verfasst von
Wei-Zhi Wu
Yee Leung
Publikationsdatum
04.05.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2020
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-00954-1

Weitere Artikel der Ausgabe 5/2020

International Journal of Machine Learning and Cybernetics 5/2020 Zur Ausgabe

Original Article

Similarity-based attribute reduction in rough set theory: a clustering perspective

Editorial

New advances in three-way decision, granular computing and concept lattice

Original Article

A general conflict analysis model based on three-way decision

Original Article

Complex network analysis of three-way decision researches

Original Article

Using single axioms to characterize L-rough approximate operators with respect to various types of L-relations

Original Article

Optimal scale selection by integrating uncertainty and cost-sensitive learning in multi-scale decision tables

Neuer Inhalt

    Bildnachweise