Abstract
We consider the question of conjuncting data and in particular the issue of the amount of information that results. It is shown that if the data being fused is non-conflicting then the maximal information is obtained by simply taking the intersection of the data. When the data is conflicting the use of the intersection can result in the fused value having less information then any of its components. In order to maximize the resulting information in this conflicting environment some meta knowledge must be introduced to adjudicate between conflicting data. Two approaches to address this problem are introduced. The first considers using only a subset of the observations to construct the fused value, a softening of the requirement that all observations be used. The basic rational of this approach is to calculate the fused value from a subset of observations that are not to conflicting and consisting of enough of the observations to be considered a credible fusion. Central to this approach is the introduction of meta-knowledge in the form of a measure of credibility associated with the use of different subsets of the observations. The second approach is based upon the introduction of a prioritization of the observations. In this approach an observation is essentially discounted if conflicts with higher priority observations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zadeh, L. A., “A theory of approximate reasoning,” in Machine Intelligence, Vol. 9, Hayes, J., Michie, D., and Mikulich, L.I. (eds.), New York: Halstead Press, 149–194, 1979.
Yager, R. R., “Deductive approximate reasoning systems,” IEEE Transactions on Knowledge and Data Engineering 3, 399–414, 1991.
Dubois, D. and Prade, H., “Fuzzy sets in approximate reasoning Part I: Inference with possibility distributions,” Fuzzy Sets and Systems 40, 143–202, 1991.
Zadeh, L. A., “Fuzzy sets as a basis for a theory of possibility,” Fuzzy Sets and Systems 1, 3–28, 1978.
Yager, R. R., “Entropy and specificity in a mathematical theory of evidence,” Int. J. of General Systems 9, 249–260, 1983.
Yager, R. R., “Measures of specificity for possibility distributions,” in Proc. of IEEE Workshop on Languages for Automation: Cognitive Aspects in Information Processing, Palma de Mallorca, Spain, 209–214, 1985.
Yager, R. R., “On the specificity of a possibility distribution,” Fuzzy Sets and Systems 50, 279–292, 1992.
Yager, R. R., “Default knowledge and measures of specificity,” Information Sciences 61, 1–44, 1992.
Zadeh, L. A., “Fuzzy sets and information granularity,” in Advances in Fuzzy Set Theory and Applications, Gupta, M.M., Ragade, R.K., and Yager, R.R. (eds.), Amsterdam: North-Holland, 3–18, 1979.
Dubois, D. and Prade, H., Possibility Theory: An Approach to Computerized Processing of Uncertainty, Plenum Press: New York, 1988.
Zadeh, L. A., “PRUF-a meaning representation language for natural languages,” International Journal of Man-Machine Studies 10, 395–460, 1978.
Yager, R. R., Ovchinnikov, S., Tong, R. and Nguyen, H., Fuzzy Sets and Applications: Selected Papers by L. A. Zadeh, John Wiley, and Sons: New York, 1987.
Yager, R. R., “Approximate reasoning as a basis for rule based expert systems,” IEEE Trans. on Systems, Man and Cybernetics 14, 636–643, 1984.
Dubois, D. and Prade, H., “Fuzzy sets in approximate reasoning Part 2: logical approaches,” Fuzzy Sets 40, 203–244, 1991.
Yager, R. R., “Conflict resolution in the fusion of fuzzy knowledge via information maximization,” International Journal of General Systems, (To Appear).
Sugeno, M., “Theory of fuzzy integrals and its application,” Doctoral Thesis, Tokyo Institute of Technology, 1974.
Sugeno, M., “Fuzzy measures and fuzzy integrals: a survey,” in Fuzzy Automata and Decision Process, Gupta, M.M., Saridis, G.N., and Gaines, B.R. (eds.), Amsterdam: North-Holland Pub, 89–102, 1977.
Yager, R. R., “A general approach to criteria aggregation using fuzzy measures,” International Journal of Man-Machine Studies 38, 187–213, 1993.
Yager, R. R., “Constrained OWA aggregation,” Fuzzy Sets and Systems 81, 89–101, 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Yager, R.R. (2000). Maximizing the Information Obtained from Data Fusion. In: Fodor, J., De Baets, B., Perny, P. (eds) Preferences and Decisions under Incomplete Knowledge. Studies in Fuzziness and Soft Computing, vol 51. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1848-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1848-2_6
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2474-2
Online ISBN: 978-3-7908-1848-2
eBook Packages: Springer Book Archive