Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Intelligent Mentoring Bots in Learning Management Systems Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Research on Uncertain Prediction Method Based on Credibility Distribution

Authors : Yan Li, Chenxia Jin, Ying Wang

Published in: Learning Technologies and Systems

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

With the rapid development of information society, traditional data mining is unable to meet the actual needs. In this paper, an uncertain prediction method based on credibility distribution (RDP) is proposed. Firstly, the implementation mechanism of the uncertain prediction based on credibility distribution in sampling is given. Secondly, combining with the law of large Numbers, the convergence characteristics of test credibility of the decision attribute corresponding to the value of a conditional attribute in sampling are analyzed. Finally, the validity of RDP is verified through Simulation experiment of UCI database. Theoretical analysis and simulation results show that RDP is feasible in interpretability and operability.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter Research on Attribute Reduction Method Based on Local Dependency
next chapter Variable Precision Multi-granularity Rough Soft Sets Based on Multiple Thresholds
Literature
1.
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRef
2.
Cooper, G.F., Herskovits, E.A.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–347 (1992)MATH
3.
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
4.
Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. (IJCIS) 11, 341–356 (1982)
5.
Das, A.K., Sengupta, S., Bhattacharyya, S.: A group incremental feature selection for classification using rough set theory based genetic algorithm. Appl. Soft Comput. 65, 400–411 (2018)CrossRef
6.
Cerri, R., Basgalupp, M.P., Barros, R.C., de Carvalho, A.C.P.L.F.: Inducing hierarchical multi-label classification rules with genetic algorithms. Appl. Soft Comput. 77, 584–604 (2019)
7.
Wu, J., Pan, S., Zhu, X., Cai, Z., Zhang, P., Zhang, C.: Self-adaptive attribute weighting for Naive Bayes classification. Expert Syst. Appl. 42(3), 1487–1502 (2015)CrossRef
8.
Karabadji, N.E.I., Khelf, I., Seridi, H., Aridhi, S., Remond, D., Dhifli, W.: A data sampling and attribute selection strategy for improving decision tree construction. Expert Syst. Appl. 129, 84–96 (2019)CrossRef
9.
10.
Trabelsi, A., Elouedi, Z., Lefevre, E.: Decision tree classifiers for evidential attribute values and class labels. Fuzzy SetSyst. 366, 46–62 (2019)MathSciNetCrossRef
11.
Obregon, J., Kim, A., Jung, J.Y.: RuleCOSI: combination and simplification of production rules from boosted decision trees for imbalanced classification. Expert Syst. Appl. 126, 64–82 (2019)CrossRef
12.
Mao, S., Cheng, Y., Pu, X.: Course of probability theory and mathematical statistics. Higher Education Press, Peking (2004)
Metadata
Title
Research on Uncertain Prediction Method Based on Credibility Distribution
Authors
Yan Li
Chenxia Jin
Ying Wang
Copyright Year
2021
Book
Learning Technologies and Systems

Print ISBN: 978-3-030-66905-8

Electronic ISBN: 978-3-030-66906-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-66906-5_14

Premium Partner

    Image Credits