Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 2.30 MB
Add to list
  1. Search results
  2. Imputing missing values in nuclear sa...

Imputing missing values in nuclear safeguards evaluation by a 2-tuple computational model

Rosa M Rodríguez, Da Ruan (UGent) , Jun Liu, Alberto Calzada and Luis Martínez
(2010) Lecture Notes in Artificial Intelligence. In Lecture Notes in Artificial Intelligence 6113. p.202-209
Author
Organization
Abstract
Nuclear safeguards evaluation aims to verify that countries are not misusing nuclear programs for nuclear weapons purposes. Experts of the International Atomic Energy Agency (IAEA) evaluate many indicators by using diverse sources, which are vague and imprecise. The use of linguistic information has provided a better way to manage such uncertainties. However, missing values in the evaluation are often happened because there exist many indicators and the experts have not sufficient knowledge or expertise about them. Those missing values might bias the evaluation result. In this contribution, we provide an imputation process based on collaborative filtering dealing with the linguistic 2-tuple computation model and a trust measure to cope with such problems.
Keywords
imputation, fuzzy sets, trust worthy, INFORMATION, CLASSIFICATION, SYSTEMS, nuclear safeguards, missing values

Downloads

  • Download
    (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 2.30 MB

Citation

Please use this url to cite or link to this publication:

MLA
Rodríguez, Rosa M, Da Ruan, Jun Liu, et al. “Imputing Missing Values in Nuclear Safeguards Evaluation by a 2-tuple Computational Model.” Lecture Notes in Artificial Intelligence. Ed. Leszek Rutkowski et al. Vol. 6113. Berlin, Germany: Springer, 2010. 202–209. Print.
APA
Rodríguez, R. M., Ruan, D., Liu, J., Calzada, A., & Martínez, L. (2010). Imputing missing values in nuclear safeguards evaluation by a 2-tuple computational model. In L. Rutkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, & J. M. Zurada (Eds.), Lecture Notes in Artificial Intelligence (Vol. 6113, pp. 202–209). Presented at the 10th International conference on Artificial Intelligence and Soft Computing (ICAISC 2010), Berlin, Germany: Springer.
Chicago author-date
Rodríguez, Rosa M, Da Ruan, Jun Liu, Alberto Calzada, and Luis Martínez. 2010. “Imputing Missing Values in Nuclear Safeguards Evaluation by a 2-tuple Computational Model.” In Lecture Notes in Artificial Intelligence, ed. Leszek Rutkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A Zadeh, and Jacek M Zurada, 6113:202–209. Berlin, Germany: Springer.
Chicago author-date (all authors)
Rodríguez, Rosa M, Da Ruan, Jun Liu, Alberto Calzada, and Luis Martínez. 2010. “Imputing Missing Values in Nuclear Safeguards Evaluation by a 2-tuple Computational Model.” In Lecture Notes in Artificial Intelligence, ed. Leszek Rutkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A Zadeh, and Jacek M Zurada, 6113:202–209. Berlin, Germany: Springer.
Vancouver
1.
Rodríguez RM, Ruan D, Liu J, Calzada A, Martínez L. Imputing missing values in nuclear safeguards evaluation by a 2-tuple computational model. In: Rutkowski L, Scherer R, Tadeusiewicz R, Zadeh LA, Zurada JM, editors. Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer; 2010. p. 202–9.
IEEE
[1]
R. M. Rodríguez, D. Ruan, J. Liu, A. Calzada, and L. Martínez, “Imputing missing values in nuclear safeguards evaluation by a 2-tuple computational model,” in Lecture Notes in Artificial Intelligence, Zakopane, Poland, 2010, vol. 6113, pp. 202–209.
@inproceedings{2918655,
  abstract     = {Nuclear safeguards evaluation aims to verify that countries are not misusing nuclear programs for nuclear weapons purposes. Experts of the International Atomic Energy Agency (IAEA) evaluate many indicators by using diverse sources, which are vague and imprecise. The use of linguistic information has provided a better way to manage such uncertainties. However, missing values in the evaluation are often happened because there exist many indicators and the experts have not sufficient knowledge or expertise about them. Those missing values might bias the evaluation result. In this contribution, we provide an imputation process based on collaborative filtering dealing with the linguistic 2-tuple computation model and a trust measure to cope with such problems.},
  author       = {Rodríguez, Rosa M and Ruan, Da and Liu, Jun and Calzada, Alberto and Martínez, Luis},
  booktitle    = {Lecture Notes in Artificial Intelligence},
  editor       = {Rutkowski, Leszek and Scherer, Rafal and Tadeusiewicz, Ryszard and Zadeh, Lotfi A and Zurada, Jacek M},
  isbn         = {9783642132070},
  issn         = {0302-9743},
  keywords     = {imputation,fuzzy sets,trust worthy,INFORMATION,CLASSIFICATION,SYSTEMS,nuclear safeguards,missing values},
  language     = {eng},
  location     = {Zakopane, Poland},
  pages        = {202--209},
  publisher    = {Springer},
  title        = {Imputing missing values in nuclear safeguards evaluation by a 2-tuple computational model},
  url          = {http://dx.doi.org/10.1007/978-3-642-13208-7_26},
  volume       = {6113},
  year         = {2010},
}
Altmetric
View in Altmetric
Web of Science
Times cited: