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 11/2019

20.09.2019 | Original Article

Double-quantitative multigranulation decision-theoretic rough fuzzy set model

verfasst von: Mengmeng Li, Minghao Chen, Weihua Xu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 11/2019

Einloggen

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

search-config
loading …

Abstract

Probabilistic rough set model and graded rough set model are used to measure relative quantitative information and absolute quantitative information between equivalence classes and basic concepts, respectively. Since fuzzy concepts are more common in real life than classical concepts, how to use relative and absolute quantitative information to determine fuzzy concepts is a extremely important research topic. In this study, we propose a double-quantitative decision theory rough fuzzy set frame based on the fusion of decision theory rough set and graded rough set, and the framework mainly studies the fuzzy concepts in multigranulation approximation spaces. Three pairs of double-quantitative multigranulation decision theory rough fuzzy set models are established. Some basic characteristics of these models are discussed. The decision rules including relative and absolute quantitative information are studied. The intrinsic relationship between the double-quantitative decision theory rough fuzzy set and the multigranulation rough set is analyzed. Finally, an illustrative case of medical diagnosis is conducted to explain and evaluate the dual quantitative decision theory approach.
Vorheriger Artikel Fuzzy rough clustering for categorical data
Nächster Artikel A knowledge acquisition method based on concept lattice and inclusion degree for ordered information systems

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.
Pawlak Z (1982) Rough set. Int J Comput Inf Sci 11(5):341–356MATH
2.
Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic, DordrechtMATH
3.
Zhao XR, Hu BQ (2016) Fuzzy probabilistic rough sets and their corresponding three-way decisions. Knowl Based Syst 91:126–142
4.
Zhang XH, Miao DQ, Liu CH, Le ML (2016) Constructive methods of rough approximation operators and multigranulation rough sets. Knowl Based Syst 91:114–125
5.
Zhang YL, Li CQ, Lin ML, Lin YJ (2015) Relationships between generalized rough sets based on covering and reflexive neighborhood system. Inf Sci 319:56–67MathSciNetMATH
6.
7.
Zhang QH, Zhang Q, Wang GY (2016) The uncertainty of probabilistic rough sets in multigranulation spaces. Int J Approx Reason 77:38–54MATH
8.
Liang JY, Chin KS, Dang C et al (2002) A new method for measuring uncertainty and fuzziness in rough set theory. Int J Gen Syst 31(4):331–342MathSciNetMATH
9.
Chen DG, Kwong S, He Q, Wang H (2012) Geometrical interpretation and applications of membership functions with fuzzy rough sets. Fuzzy Sets Syst 193:122–135MathSciNetMATH
10.
Tan AH, Li JJ, Lin GP (2015) Extended results on the relationship between information systems. Inf Sci 290:156–173MathSciNetMATH
11.
Liang JY, Wang JH, Qian YH (2014) A new measure of uncertainty based on knowledge granulation for rough sets. Inf Sci 26(2):294–308
12.
Wu WZ, Zhang M, Li HZ, Mi JS (2005) Knowledge reduction in random information systems via Dempster-Shafer theory of evidence. Inf Sci 174(3):143–164MathSciNetMATH
13.
Zhu W (2009) Relationship among basic concepts in covering-based rough sets. Inf Sci 179(14):2478–2486MathSciNetMATH
14.
15.
Tan AH, Wu WZ, Li JJ, Lin GP (2016) Evidence-theory-based numerical characterization of multigranulation rough sets in incomplete information systems. Fuzzy Sets Syst 294:18–35MathSciNetMATH
16.
Wang GY, Ma XA, Yu H (2015) Monotonic uncertainty measures for attribute reduction in probabilistic rough set model. Int J Approx Reason 59:41–67MathSciNetMATH
17.
Xue Z, Zeng X, Koehl L, Chen Y (2014) Measuring consistency of two datasets using fuzzy techniques and the concept of indiscernibility: application to human perceptions on fabrics. Eng Appl Artif Intell 36:54–63
18.
Chen DG, Li WL, Kwong S (2014) Evidence-theory-based numerical algorithms of attribute reduction with neighborhood-covering rough sets. Int J Approx Reason 55(3):908–923MathSciNetMATH
19.
Chen DG, Zhang XX, Li WL (2015) On measurements of covering rough sets based on granules and evidence theory. Inf Sci 317:329–348MathSciNetMATH
20.
Pawlak Z, Wong SK, Ziarko W (1988) Rough sets: probabilistic versus deterministic approach. Int J Man Mach Stud 29(1):81–95MATH
21.
Wong SKM, Ziarko W (1987) INFER: an adaptative decision support system based on the probabilistic approximate classification. International workshop on expert systems and their applications, pp 713–726
22.
Wong SKM, Ziarko W (1987) Comparison of the probabilistic approximate classification and the fuzzy set model. Fuzzy Sets Syst 21(3):357–362MathSciNetMATH
23.
Yao YY, Wong SKM, Lingras P (1990) A decision-theoretic rough set model. Methodologies for Intelligent Systems, North-Holland, New York
24.
Pawlak Z, Skowron A (1994) Rough membership functions. Advances in the Dempster–Shafer Theory of Evidence. Wiley, New York, pp 251–271
25.
26.
Slezak D, Ziarko W (2005) The investigation of the Bayesian rough set model. Int J Approx Reason 40:81–91MathSciNetMATH
27.
28.
Yao YY, Zhou B (2010) Naive Bayesian rough sets. In: Proceedings of the 5th international conference on rough sets and knowledge technology, vol 6401. LNAI, pp 713–720
29.
Zhang XY, Miao DQ (2015) An expanded double-quantitative model regarding probabilities and grades and its hierarchical double-quantitative attribute reduction. Inf Sci 299:312–336MathSciNetMATH
30.
Zhang XY, Miao DQ (2016) Double-quantitative fusion of accuracy and importance: systematic measure mining, benign integration construction, hierarchical attribute reduction. Knowl Based Syst 91:219–240
31.
Zhang XY, Miao DQ (2013) Two basic double-quantitative rough set models for precision and graded and their investigation using granular computing. Int J Approx Reason 54:1130–1148MATH
32.
Zhang XY, Miao DQ (2014) Quantitative information architecture, granular computing and rough set models in the double-quantitative approximation space of precision and grade. Inf Sci 268(2):147–168MathSciNetMATH
33.
Zadeh LA (1979) Fuzzy sets and information granularity. In: Gupa N, Ragade R, Yager R (eds) Advances in fuzzy set theory and applications. North-Holland, Amsterdam, pp 3–18
34.
Qian YH, Liang JY (2006) Rough set method based on multigranulations, ICCI2006, cognitiveinformatics. IEEE Int. Conf. Cognit. Inf. pp 297-304
35.
Malyszko D, Stepaniuk J (2010) Adaptive multilevel rough entropy evolutionary thresholding. Inf Sci 180(7):1138–1158MathSciNetMATH
36.
Wu WZ, Leung Y (2011) Theory and applications of granular labelled partitions in multi-scale decision tables. Inf Sci 181(18):3878–3897MATH
37.
Qian YH, Liang JY, Yao YY, Dang CY (2010) Mgrs: a multi-granulation rough set. Inf Sci 180:949–970MathSciNetMATH
38.
Lin YJ, Li JJ, Lin PR, Lin GP, Chen JK (2014) Feature selection via neighborhood multigranulation fusion. Knowl Based Syst 67:162–168
39.
Qian YH, Liang JY, Dang CY (2010) Incomplete mutigranulation rough set. IEEE Trans Syst Man Cybern Part A 40:420–431
40.
Qian YH, Liang JY, Pedrycz W, Dang CY (2010) Positive approximation: an accelerator for attribute reduction in rough set theory. Artif Intell 174:597–618MathSciNetMATH
41.
Qian YH, Liang JY, Pedrycz W, Dang CY (2011) An efficient accelerator for attribute reduction from incomplete data in rough set framework. Pattern Recogn 44:1658–1670MATH
42.
Dai JH, Hu H, Wu WZ, Qian YH, Huang DB (2018) Maximal-discernibility-pair-based approach to attribute reduction in fuzzy rough sets. IEEE Trans Fuzzy Syst 26(4):2174–2187
43.
Yang XB, Qi Y, Yu HL, Song XN, Yang JY (2014) Updating multigranulation rough approximations with increasing of granular structures. Knowl Based Syst 64:59–69
44.
Dai JH, Xu Q (2013) Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl Soft Comput 13(1):211–221
45.
Qian YH, Zhang H, Sang YL, Liang JY (2014) Multigranulation decision-theoretic rough sets. Int J Approx Reason 55:225–237MathSciNetMATH
46.
She YH, He XL (2012) On the structure of the multigranulation rough set model. Knowl Based Syst 36:81–92
47.
Wu MF (2010) Fuzzy rough set model based on multi-granulations. Int Conf Comput Eng Technol 2010:271–275
48.
Yang XB, Song XN, Dou HL (2011) Multi-granulation rough set: from crisp to fuzzy case. Ann Fuzzy Math Inf 1(1):55–70MathSciNetMATH
49.
Xu WH, Wang QR, Zhang XT (2011) Multi-granulation fuzzy rough set models on tolerance relations. In: Proceeding of The Fourth International Workshop on Advanced Computational Intelligence. Wuhan, Hubei, pp 359–366
50.
Liu CH, Wang MZ (2011) Covering fuzzy rough set based on multigranulations. In: Proceedings of International Conference on Uncertainty Reasoning and Knowledge Engineering, pp 146–149
51.
Liu CH, Miao DQ (2011) Covering rough set model based on multi-granulations, RSFD-GRc2011 87-90
52.
Liang JY, Wang F, Dang CY, Qian YH (2012) An efficient rough feature selection algorithm with a multi-granulation view. Int J Approx Reason 53(6):912–926MathSciNet
53.
54.
55.
Yao YY, Lin TY (1996) Generalization of rough sets using modal logic. Intell. Autom. Soft Comput. 2(2):103–119
56.
Yao YY, Wong SKM (1992) A decision theoretic framework for approximating concepts. Int J Man Mach Stud 37(6):793–809
57.
Sun BZ, Ma WM, Zhao HY (2014) Decision-theoretic rough fuzzy set model and application. Inf Sci 283:180–196MathSciNetMATH
Metadaten
Titel
Double-quantitative multigranulation decision-theoretic rough fuzzy set model
verfasst von
Mengmeng Li
Minghao Chen
Weihua Xu
Publikationsdatum
20.09.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 11/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-01013-5

Weitere Artikel der Ausgabe 11/2019

International Journal of Machine Learning and Cybernetics 11/2019 Zur Ausgabe

Original Article

A decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy

Original Article

A theoretical study on object-oriented and property-oriented multi-scale formal concept analysis

Original Article

An ensemble multiscale wavelet-GARCH hybrid SVR algorithm for mobile cloud computing workload prediction

Original Article

Neural variational collaborative filtering with side information for top-K recommendation

Original Article

Ordered smooth representation clustering

Original Article

Dynamic time alignment kernel-based fuzzy clustering of non-equal length vector time series

Neuer Inhalt

    Bildnachweise