Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33092
The Optimization of Decision Rules in Multimodal Decision-Level Fusion Scheme
Authors: Andrey V. Timofeev, Dmitry V. Egorov
Abstract:
This paper introduces an original method of parametric optimization of the structure for multimodal decisionlevel fusion scheme which combines the results of the partial solution of the classification task obtained from assembly of the mono-modal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained.
Keywords: Сlassification accuracy, fusion solution, total error rate.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099654
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1974References:
[1] Dave L. Hall and James Linas, “Introduction to Multisensor Data Fusion”, Proc. of IEEE, Vol. 85, No. 1, pp. 6 – 23, Jan 1997.
[2] ISO/IEC JTC 1/SC 37 N 1506, Biometrics, 2006-02-28.
[3] Erik Blasch, Ivan Kadar, John Salerno, Mieczyslaw Kokar, Subrata Das, Gerald Powell, Daniel Corkill, and E. Euspini, “Issues and Challenges in Situation Assessment (Level 2 Fusion)”, Journal of Advances in Information Fusion, Vol 1, No 2, Dec. (2006).
[4] Liggins, Martin E., David L. Hall, and James Llinas. “Multisensor Data Fusion, Second Edition Theory and Practice (Multisensor Data Fusion)”. CRC, (2008).
[5] David L. Hall, Sonya A. H. McMullen, “Mathematical Techniques in Multisensor Data Fusion”, Artech House (2004)
[6] H. B. Mitchell, “Multi-sensor Data Fusion – An Introduction” Springer- Verlag, Berlin 2007)
[7] A. C. Kak, Su-Shing, Spatial reasoning and multi-sensor fusion: proceedings of the 1987 workshop, American Association for Artificial Intelligence, 1987: Saint Charles III.
[8] L., Xu, A., Kryzak, C.Y., Suen, "Methods of Combining Multiple Classifiers and Their Application to Handwriting Recognition", IEEE Trans. on Systems, Man and Cyber.,1992, vol. 22, no. 3, pp. 418-435.