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 12/2018

27.04.2017 | Original Article

Dynamic maintenance of approximations under fuzzy rough sets

verfasst von: Yi Cheng

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 12/2018

Einloggen

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

search-config
loading …

Abstract

The lower and upper approximations are basic concepts in rough set theory. Approximations of a concept in rough set theory need to be updated for dynamic data mining and related tasks. Most existing incremental methods are based on the classical rough set model and limited to describing crisp concepts. This paper presents two new dynamic methods for incrementally updating the approximations of a concept under fuzzy rough sets to describe fuzzy concepts, one starts from the boundary set, the other is based on the cut sets of a fuzzy set. Some illustrative examples are conducted. Then two algorithms corresponding to the two incremental methods are put forward respectively. The experimental results show that the two incremental methods effectively reduce the computing time in comparison with the traditional non-incremental method.
Vorheriger Artikel Detection and localization of crowd behavior using a novel tracklet-based model
Nächster Artikel Robust fractional PID controller synthesis approach for the permanent magnetic synchronous motor

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.
2.
Dubois D, Prade H (1990) Fuzzy rough sets and fuzzy rough sets. Int J Gen Syst 17(2–3):191–209CrossRef
3.
4.
5.
Shen Q, Jensen R (2004) Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring. Pattern Recognit 37:1351–1363CrossRef
6.
Asharafa S, Narasimha Murty M (2003) An adaptive rough fuzzy single pass algorithm for clustering large data sets. Pattern Recognit 36(12):3015–3018CrossRef
7.
Asharafa S, Narasimha Murty M (2004) A rough fuzzy approach to web usage categorization. Fuzzy Sets Syst 148(1):119–129MathSciNetCrossRef
8.
Mi J, Zhang W (2004) An axiomatic characterization of a fuzzy generalization of rough sets. Inf Sci 160(1–4):235–249MathSciNetCrossRef
9.
Wu W, Zhang W (2004) Constructive and axiomatic approaches of fuzzy approximation operators. Inf Sci 159(3–4):233–254MathSciNetCrossRef
10.
11.
Cheng Y (2011) The incremental method for fast computing the rough fuzzy approximations. Data Knowl Eng 70:84–100CrossRef
12.
Cheng Y, Miao D, Feng Q (2011) Positive approximation and converse approximation in interval-valued fuzzy rough sets. Inf Sci 181:2086–2110MathSciNetCrossRef
13.
Cheng Y, Miao D (2011) Rules induction based on granulation in interval-valued fuzzy information system. Expert Syst Appl 38:12249–12261CrossRef
14.
Cheng Y (2012) A new approach for rule extraction in fuzzy information systems. J Comput Inf Syst 21:8795–8805
15.
Michalski RS (1985) Knowledge repair mechanisms: evolution vs. revolution. In: Proceedings of the 3rd international machine learning workshop, pp 116–119.
16.
Bouchachia A, Mittermeir R (2007) Towards incremental fuzzy classifiers. Soft Comput 11(2):193–207CrossRef
17.
Bang W, nam Bien Z (1999) New incremental learning algorithm in the framework of rough set theory. Int J Fuzzy Syst 1:25–36MathSciNet
18.
Zheng Z, Wang G (2004) RRIA: a rough set and rule tree based incremental knowledge acquisition algorithm. Fundam Inf 59(2–3):299–313MathSciNetMATH
19.
Wang L, Wu Y, Wang G (2005) An incremental rule acquisition algorithm based on variable precision rough set model. J Chongqing Univ Posts Telecommun Nat Sci 17(6):709–713
20.
Zhang J, Li T, Ruan D, Liu D (2012) Neighborhood rough sets for dynamic data mining. Int J Intell Syst 27:317–342CrossRef
21.
Li S, Li T, Liu D (2013) Dynamic maintenance of approximations in dominance-based rough set approach under the variation of the object set. Int J Intell Syst 28(8):729–751MathSciNetCrossRef
22.
Luo C, Li T, Chen H, Liu D (2013) Incremental approaches for updating approximations in set-valued ordered information systems. Knowl-Based Syst 50:218–233CrossRef
23.
24.
Zeng A, Li T, Luo C (2013) An incremental approach for updating approximations of gaussian Kernelized fuzzy rough sets under the variation of the object set. Comput Sci (in Chin) 40(7):20–27
25.
Wang S, Li T, Luo C, Fujita H (2016) Efficient updating rough approximations with multi-dimensional variation of ordered data. Inf Sci 372:690–708CrossRef
26.
Luo C, Li T, Chen H, Fujita H, Yi Z (2016) Efficient updating of probabilistic approximations with incremental objects. Knowl-Based Syst 109:71–83CrossRef
27.
Chen H, Li T, Luo C, Horng S-J, Wang G (2014) A rough set-based method for updating decision rules on attribute values’ coarsening and refining. IEEE Trans Knowl Data Eng 26(12):2886–2899CrossRef
28.
Luo C, Li T, Chen H, Lu L (2015) Fast algorithms for computing rough approximations in set-valued decision systems while updating criteria values. Inf Sci 299:221–242MathSciNetCrossRef
29.
Zeng A, Li T, Hua J, Chen H, Luo C (2017) Dynamical updating fuzzy rough approximations for hybrid data under the variation of attribute values. Inf Sci 378:363–388MathSciNetCrossRef
30.
Chan CC (1998) A rough set approach to attribute generalization in data mining. Inf Sci 107(1–4):177–194MathSciNet
31.
Liu S, Sheng Q, Shi Z (2003) A new method for fast computing positive region. J Comput Res Dev (in Chin) 40(5):637–642
32.
Li T, Ruan D, Geert W (2007) A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowl-Based Syst 20(5):485–494CrossRef
33.
Zhang J, Li T, Liu D (2010) An approach for incremental updating approximations in variable precision rough sets while attribute generalizing. In: Proceedings of 2010 IEEE international conference on intelligent systems and knowledge engineering, pp 77–81
34.
Li S, Li T, Liu D (2013) Incremental updating approximations in dominance-based rough sets approach under the variation of the attribute set. Knowl-Based Syst 40:17–26CrossRef
35.
Luo C, Li T, Chen H (2014) Dynamic maintenance of approximations in set-valued ordered decision systems under the attribute generalization. Inf Sci 257:210–228MathSciNetCrossRef
36.
Liu D, Li T, Zhang J (2015) Incremental updating approximations in probabilistic rough sets under the variation of attributes. Knowl-Based Syst 73:81–96CrossRef
37.
Zhang Y, Li T, Luo C (2016) Incremental updating of rough approximations in interval-valued information systems under attribute generalization. Inf Sci 373:461–475CrossRef
38.
Zeng A, Li T, Liu D, Zhang J, Chen H (2015) A fuzzy rough set approach for incremental feature selection on hybrid information systems. Fuzzy Sets Syst 258:39–60MathSciNetCrossRef
39.
Chen H, Li T, Luo C, Horng S-J, Wang G (2015) A decision-theoretic rough set approach for dynamic data mining. IEEE Trans Fuzzy Syst 23(6):1958–1970CrossRef
40.
Liu D, Li T, Zhang J (2014) A rough set-based incremental approach for learning knowledge in dynamic incomplete information systems. Int J Approx Reason 55:1764–1786MathSciNetCrossRef
41.
Zhang J, Wong J-S, Pan Y, Li T (2015) A parallel matrix-based method for computing approximations in incomplete information systems. IEEE Trans Knowl Data Eng 27(2):326–339CrossRef
42.
Luo C, Li T, Chen H, Lu L (2016) Matrix approach to decision-theoretic rough sets for evolving data. Knowl-Based Syst 99:123–134CrossRef
43.
Zhang J, Zhu Y, Pan Y, Li T (2016) Efficient parallel Boolean matrix based algorithms for computing composite rough set approximations. Inf Sci 329:287–302CrossRef
44.
Liu D, Li T, Ruan D (2009) An incremental approach for inducing knowledge from dynamic information systems. Fundam Inf 94:245–260MathSciNetMATH
45.
Liu D, Li T, Ruan D, Zhang J (2011) Incremental learning optimization on knowledge discovery in dynamic business intelligent systems. J Glob Optim 51:325–344MathSciNetCrossRef
46.
Yao Y (1997) Combination of rough and fuzzy sets based on level sets, rough sets and data mining: analysis for imprecise data. Kluwer Academic, Dordrecht, pp 301–321
47.
Wang X, Tsang ECC, Zhao S, Chen D, Yeung DS (2007) Learning fuzzy rules from fuzzy samples based on rough set technique. Inf Sci 177:4493–4514MathSciNetCrossRef
48.
49.
Yuan Y, Shaw MJ (1995) Introduction of fuzzy decision tree. Fuzzy Sets Syst 69:125–139CrossRef
50.
Kohonen T (1988) Self-organization and associative memory. Springer, BerlinCrossRef
Metadaten
Titel
Dynamic maintenance of approximations under fuzzy rough sets
verfasst von
Yi Cheng
Publikationsdatum
27.04.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 12/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0683-7

Weitere Artikel der Ausgabe 12/2018

International Journal of Machine Learning and Cybernetics 12/2018 Zur Ausgabe

Original Article

Domain invariant feature extraction against evasion attack

Original Article

Self-organizing weighted incremental probabilistic latent semantic analysis

Original Article

A line symmetry based genetic clustering technique: encoding lines in chromosomes

Original Article

Pornographic video detection with MapReduce

Original Article

Generalized eigenvalue proximal support vector regressor for the simultaneous learning of a function and its derivatives

Original Article

Delay-dependent robust absolute stability of uncertain Lurie singular systems with neutral type and time-varying delays

Neuer Inhalt

    Bildnachweise