Data imbalance SEI method based on dynamic weight model
To tackle with the problem of decreased recognition accuracy caused by imbalanced individual data distribution in Specific Emitter Identification(SEI),a dynamic weight model based method is proposed for individual identification of radiation sources.A Dynamic Class Weight(DCW)model is built.A moderate initial weight value is obtained by using a meta learning algorithm through two-layer calculation with a small amount of sample data.Then,a new cost sensitive loss function is designed to calculate the backward adjustment of the distance between the predicted value and the true value,which gives the minority learning weight,and moderately increases the attention to the minority data.It is more friendly to the minority.It has obvious advantages in the processing of highly unbalanced data,which alleviates the calculation misleading of the majority of samples in the whole recognition process,thus improving the overall recognition accuracy.
Specific Emitter Identificationunbalanced dataDynamic Class Weightsmeta learningcost sensitive losses
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