This paper proposes an SEI method based on cost-sensitive learning and semisupervised generative adversarial networks to address the problem of incomplete sample labels and imbalanced data category distribution in Specific Emitter Identification (SEI). which leads to a decline in inaccuracy. Through semisupervised training. the method optimizes the network parameters of the generator ... https://www.ealisboa.com/