Abstract:

Aiming at the problem that the measures of effectiveness (MOE) of system of systems in operational simulation experiment usually depends on expert evaluation with relatively high costs, this paper proposes an evaluation method of MOE based on pre-clustering active semi-supervised learning. This paper presents the basic process of using this method to evaluate the MOE of operational system of systems, the top-down evaluation model, and binarized evaluation criteria of this evaluation method. This paper focuses on constructing the pre-clustering active semi-supervised learning algorithm. Firstly, according to the characteristics of combat simulation experiment data, this method pre-clusters the unevaluated samples, and selects the most valuable samples for experts to mark. Secondly, it uses the evaluated samples to train the common learners of the active learning algorithm and the semi-supervised learning algorithm. Finally, it uses the active learning algorithm to select the high value samples for experts to evaluate, and it uses the new samples to continuously update the learners. Simulation results show that the method can reduce the evaluation cost while achieving the expected evaluation accuracy, and can be effectively applied to MOE evaluation of large-scale combat simulation experiments.

Key words: pre-clustering, active learning, semi-supervised learning, operational system of systems, measures of effectiveness (MOE)