Abstract
We propose in this paper a military application, which can be used in civil contexts as well, for solving inverse classification problems. Pattern recognition and decision support systems are typical tools through which inverse classification problems can be solved in order to achieve the desired goals. As standard classifiers do not work properly for inverse classification, which is an inherent ill-posed problem and therefore difficult to be inverted, we propose a new approach that exploits all the information associated with the decisions observed in the past. The experimental results prove the feasibility of the proposed algorithm, with errors lower than 10% with respect to standard classification models.
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U.S. Army, Joint Staff: Joint Targeting. Joint Publication 3-60, January 31 (2013). https://www.justsecurity.org/wp-content/uploads/2015/06/Joint_Chiefs-Joint_Targeting_20130131.pdf
NATO, NSA: Allied Joint Doctrine for Information Operations AJP-3.10. NATO European High Quarter, Brussels (2012). https://info.publicintelligence.net/NATO-IO.pdf
Meyer, B.A.: Pattern Recognition for Medical Imaging. Elsevier Academic Press, London (2003)
Corr, P.: Pattern Recognition in diagnostic imaging. World Health Organization, Geneve (2001)
Rizzi, A., Buccino, M., Panella, M., Uncini, A.: Genre Classification of Compressed Audio Data. In: Proc. of IEEE Workshop on Multimedia Signal Processing (MMSP 2008), pp. 654–659 (2008)
Scardapane, S., Fierimonte, R., Wang, D., Panella, M., Uncini, A.: Distributed Music Classification Using Random Vector Functional-Link Nets. In: Proc. of International Joint Conference on Neural Networks (IJCNN 2015), pp. 272–279 (2015)
Altilio, R., Paoloni, M., Panella, M.: Selection of clinical features for pattern recognition applied to gait analysis. Medical and Biological Engineering and Computing 55(4), 685–695 (2017)
Friedman, J., Hastie, T., Tibshirani, R.: The elements of statistical learning, 2nd edn. Springer, New York (2009)
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Russo, P., Panella, M. (2020). Inverse Classification for Military Decision Support Systems. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_15
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DOI: https://doi.org/10.1007/978-981-13-8950-4_15
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