Abstract
The study of information fusion comprises methods and techniques to automatically or semi-automatically combine information stemming from homogeneous or heterogeneous sources into a representation that supports a human user’s situation awareness for the purposes of decision making. Information fusion is not an end in itself but studies, adapts, applies and combines methods, techniques and algorithms provided by many other research areas, such as artificial intelligence, data mining, machine learning and optimization, in order to customize solutions for specific tasks. There are many different models for information fusion that describe the overall process as tasks building upon each other on different levels of abstraction. Information fusion includes the analysis of information, the inference of new information and the evaluation of uncertainty within the information. Hence, uncertainty management plays a vital role within the information fusion process. Uncertainty can be expressed by probability theory or, in the form of non-specificity and discord, by, for example, evidence theory.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
URL: http://vestibular.org/understanding-vestibular-disorder/human-balance-system
Arnborg, S. (2004). Robust Bayesianism: Imprecise and paradoxical reasoning. In Proceedings of the 7th International Conference on Information Fusion (2004)
Arnborg, S. (2006). Robust Bayesianism: Relation to evidence theory. Journal of Advances in Information Fusion, 1(1), 63–74.
Bae, J., Falkman, G., Helldin, T., & Riveiro, M. (2018). Visual data analysis. In A. Said, & V. Torra (Eds.), Data Science in Practice (2018)
Blasch, E., Bossé, E., & Lambert, D. (2012). High-level information fusion management and system design (1st ed.). Norwood, MA, USA: Artech House Inc.
Blasch, E., & Plano, S.: DIFG level 5 (user refinement) issues supporting situational assessment reasoning. In International Conference on Information Fusion (2005)
Boyed, J.: The essence of winning and losing. URL http://dnipogo.org/john-r-boyd/
Costa, P. C. G., Laskey, K. B., Blasch, E., & Jousselme, A. L.: Towards unbiased evaluation of uncertainty reasoning: The UREF ontology. In International Conference on Information Fusion (2012)
Dempster, A. P. (1969). A generalization of Bayesian inference. Journal of the Royal Statistical Society, 205–247. Wiley-Blackwell (1969)
Duarte, D., & Ståhl, N. (2018) Machine learning: A concise overview. In A. Said, & V. Torra (Eds.),Data Science in Practice
Endsley, M., & Kiris, E. (1995). The out-of-the-loop performance problem and level of control in automation. Human Factors: The Journal of the Human Facors and Ergonomics Society, 37, 381–394.
Haenni, R.: Shedding new light on Zadeh’s criticism of Dempster’s rule of combination. In International Conference on Information Fusion, pp. 879–884
Hall, D. L., & Llinas, J. (2001). Handbook of Multisensor Data Fusion. CRC Press LLC
Hall, M., & McMullen, S. (2004). Mathematical Thechniques in Multisensor Data Fusion. Artech House
Hand, D. J., Smyth, P., & Mannila, H. (2001). Principles of Data Mining. Cambridge, MA, USA: MIT Press.
Harmanec, D., & Klir, G. J. (1994). Measuring total uncertainty in Dempster-Shafer theory: A novel approach. International Journal of General Systems, 405–419. Taylor & Francis
Jousselme, A. L., Liu, C., Grenier, D., & Bossé, É. (2006). Measuring ambiguety in the evidence theory. IEEE Transactions on Systems, Man, and Cybernetics, 890–903
Jousselme, A. L., Maupin, P., & Bossé, É. (2003). Uncertainty in a situation analysis perspective. In International Conference of Information Fusion, pp. 1207–1214
Karlsson, A., Johansson, R., & Andler, S. F. (2011). Characterization and empirical evaluation of bayesian and credal combination operators. Journal of Advances in Information Fusion, 6, 150–166.
Klir, G. J. (2003). An update on generalized information theory. In International Symposium on Imprecise Probability: Theories and Applications
Klir, G. J., & Smith, R. M. (2001). On measuring uncertainty and uncertainty-based information: Recent developments. Annals of Mathematics and Artificial Intelligence
Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: theory and applications. PTR, Upper Saddle River, NJ: Prentice Hall.
Sentz, K., & Ferson, S. (2002). Combination of evidence in Dempster-Shafer theory. SANDIA: Tech. rep.
Shafer, G. (1976). A mathematical theory of evidence. Princeton University Press
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal 27, 379–423, 623–656 (1948)
Smets, P. (1999). Imperfect information: Imprecision—Uncertainty. In UMIS
Smets, P. (2000). Data fusion in the transferable belief model. In Third International Conference on Information Fusion
Steinberg, A., Bowman, D., & White, F. (1999). Revisions to the JDL data fusion model. In Sensor Fusion: Architechtures, Algorithms and Applications
Torra, V., Karlsson, A., Steinhauer, H. J., & Berglund, S. (2018). Artificial intelligence. In A. Said, & V. Torra (Eds.), Data Science in Practice
Waltz, E. L. (1998). Information understanding: integrating data fusion and data mining processes. In Circuits and Systems. ISCAS’98. Proceedings of the 1998 IEEE International Symposium on, vol. 6, pp. 553–556. IEEE (1998)
Waltz, E. L., & Llinas, J. (1990). Multisensor data fusion. Norwood, MA, USA: Artech House Inc.
Winkler, R. L. (1996). Uncertainty in probabilistic risk assessment. In Reliability Engineering and System safety, pp. 127–132
Zadeh, L. A. (1984). Review of books: A mathematical theory of evidence. AI Magazine, 5, 81–83.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Steinhauer, H.J., Karlsson, A. (2019). Information Fusion. In: Said, A., Torra, V. (eds) Data Science in Practice. Studies in Big Data, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-97556-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-97556-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97555-9
Online ISBN: 978-3-319-97556-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)