Inverse classification for military decision support systems

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.