Skip to main content

2015 | OriginalPaper | Buchkapitel

Dynamic Maintenance of Rough Fuzzy Approximations with the Variation of Objects and Attributes

verfasst von : Yanyong Huang, Tianrui Li, Shi-jinn Horng

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

Verlag: Springer International Publishing

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

search-config
loading …

Abstract

In many fields including medical research, e-business and road transportation, data may vary over time, i.e., new objects and new attributes are added. In this paper, we present a method for dynamically updating approximations based on rough fuzzy sets under the variation of objects and attributes simultaneously in fuzzy decision systems. Firstly, a matrix-based approach is proposed to construct the rough fuzzy approximations on the basis of relation matrix. Then the method for incrementally computing approximations is presented, which involves the partition of the relation matrix and partly changes its element values based the prior matrices’ information. Finally, an illustrative example is employed to validate the effectiveness of the proposed method.

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!

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!

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!

Literatur
1.
Zurück zum Zitat Błaszczyński, J., Słowiński, R.: Incremental induction of decision rules from dominance-based rough approximations. Electron. Notes Theor. Comput. Sci. 82(4), 40–51 (2003)CrossRefMATH Błaszczyński, J., Słowiński, R.: Incremental induction of decision rules from dominance-based rough approximations. Electron. Notes Theor. Comput. Sci. 82(4), 40–51 (2003)CrossRefMATH
2.
Zurück zum Zitat Chen, H., Li, T., Luo, C., Horng, S., Wang, G.: A decision-theoretic rough set approach for dynamic data mining. IEEE Trans. Fuzzy Syst. PP(99), 1–1 (2015) Chen, H., Li, T., Luo, C., Horng, S., Wang, G.: A decision-theoretic rough set approach for dynamic data mining. IEEE Trans. Fuzzy Syst. PP(99), 1–1 (2015)
3.
Zurück zum Zitat Chen, H., Li, T., Qiao, S., Ruan, D.: A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values. Int. J. Intell. Syst. 25(10), 1005–1026 (2010)CrossRefMATH Chen, H., Li, T., Qiao, S., Ruan, D.: A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values. Int. J. Intell. Syst. 25(10), 1005–1026 (2010)CrossRefMATH
4.
Zurück zum Zitat Cheng, M., Fang, B., Tang, Y.Y., Zhang, T., Wen, J.: Incremental embedding and learning in the local discriminant subspace with application to face recognition. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(5), 580–591 (2010)CrossRef Cheng, M., Fang, B., Tang, Y.Y., Zhang, T., Wen, J.: Incremental embedding and learning in the local discriminant subspace with application to face recognition. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(5), 580–591 (2010)CrossRef
5.
Zurück zum Zitat Cheng, Y.: The incremental method for fast computing the rough fuzzy approximations. Data Knowl. Eng. 70(1), 84–100 (2011)CrossRef Cheng, Y.: The incremental method for fast computing the rough fuzzy approximations. Data Knowl. Eng. 70(1), 84–100 (2011)CrossRef
6.
Zurück zum Zitat Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gener. Syst. 17(2–3), 191–209 (1990)CrossRefMATH Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gener. Syst. 17(2–3), 191–209 (1990)CrossRefMATH
7.
Zurück zum Zitat Dy, J.G., Brodley, C.E.: Feature selection for unsupervised learning. J. Mach. Learn. Res. 5, 845–889 (2004)MathSciNetMATH Dy, J.G., Brodley, C.E.: Feature selection for unsupervised learning. J. Mach. Learn. Res. 5, 845–889 (2004)MathSciNetMATH
8.
Zurück zum Zitat Karasuyama, M., Takeuchi, I.: Multiple incremental decremental learning of support vector machines. In: Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 907–915. MIT Press, Cambridge (2009) Karasuyama, M., Takeuchi, I.: Multiple incremental decremental learning of support vector machines. In: Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 907–915. MIT Press, Cambridge (2009)
9.
Zurück zum Zitat Li, T., Ruan, D., Geert, W., Song, J., Xu, Y.: A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowl. Based Syst. 20(5), 485–494 (2007)CrossRef Li, T., Ruan, D., Geert, W., Song, J., Xu, Y.: A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowl. Based Syst. 20(5), 485–494 (2007)CrossRef
11.
Zurück zum Zitat Luo, C., Li, T., Chen, H.: Dynamic maintenance of approximations in set-valued ordered decision systems under the attribute generalization. Inf. Sci. 257, 210–228 (2014)MathSciNetCrossRefMATH Luo, C., Li, T., Chen, H.: Dynamic maintenance of approximations in set-valued ordered decision systems under the attribute generalization. Inf. Sci. 257, 210–228 (2014)MathSciNetCrossRefMATH
12.
Zurück zum Zitat Luo, C., Li, T., Chen, H., Liu, D.: Incremental approaches for updating approximations in set-valued ordered information systems. Knowl. Based Syst. 50, 218–233 (2013)CrossRef Luo, C., Li, T., Chen, H., Liu, D.: Incremental approaches for updating approximations in set-valued ordered information systems. Knowl. Based Syst. 50, 218–233 (2013)CrossRef
13.
Zurück zum Zitat Luo, C., Li, T., Chen, H., Lu, L.: Fast algorithms for computing rough approximations in set-valued decision systems while updating criteria values. Inf. Sci. 299, 221–242 (2015)MathSciNetCrossRefMATH Luo, C., Li, T., Chen, H., Lu, L.: Fast algorithms for computing rough approximations in set-valued decision systems while updating criteria values. Inf. Sci. 299, 221–242 (2015)MathSciNetCrossRefMATH
15.
Zurück zum Zitat Maji, P., Pal, S., Skowron, A.: Preface: pattern recognition and mining. Natural Computing, pp. 1–3 (2015) Maji, P., Pal, S., Skowron, A.: Preface: pattern recognition and mining. Natural Computing, pp. 1–3 (2015)
16.
17.
Zurück zum Zitat Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Norwell (1992)MATH Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Norwell (1992)MATH
18.
Zurück zum Zitat Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.): Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Physica-Verlag GmbH, Heidelberg (2000) Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.): Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Physica-Verlag GmbH, Heidelberg (2000)
19.
Zurück zum Zitat Xu, W., Li, W.: Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Trans. Cybern. PP(99), 1–1 (2014) Xu, W., Li, W.: Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Trans. Cybern. PP(99), 1–1 (2014)
21.
Zurück zum Zitat Zeng, A., Li, T., Zhang, J., Chen, H.: Incremental maintenance of rough fuzzy set approximations under the variation of object set. Fundam. Inform. 132(3), 401–422 (2014)MathSciNetCrossRefMATH Zeng, A., Li, T., Zhang, J., Chen, H.: Incremental maintenance of rough fuzzy set approximations under the variation of object set. Fundam. Inform. 132(3), 401–422 (2014)MathSciNetCrossRefMATH
22.
Zurück zum Zitat Zhang, J., Li, T., Ruan, D., Liu, D.: Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems. Int. J. Approx. Reason. 53(4), 620–635 (2012)MathSciNetCrossRefMATH Zhang, J., Li, T., Ruan, D., Liu, D.: Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems. Int. J. Approx. Reason. 53(4), 620–635 (2012)MathSciNetCrossRefMATH
Metadaten
Titel
Dynamic Maintenance of Rough Fuzzy Approximations with the Variation of Objects and Attributes
verfasst von
Yanyong Huang
Tianrui Li
Shi-jinn Horng
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-25783-9_16