C4ISR state monitoring method based on SVM incremental learning of imbalanced data
To the characteristic of limited historical sample of command,control,communication,and computer,intelligence,surveillance and reconnaissance(C4ISR),an incremental learning method based on support vector machines(SVM)is designed for imbalanced data.To the imbalance of normal/abnormal state samples of the system,first use the support vector to generate a part of new samples,and then use the idea of banding to generate new samples with a more uniform distribution to adjust the imbalance ratio of the original sample set.In view of the high requirements for real-time monitoring of the system and the continuous addition of new samples during operation,the classification model is continuously updated by incremental learning.On the basis of relaxing the KKT(Karush-Kuhn-Tucker)update triggering conditions,by defining the sample importance and the introduction of retention rate/forgetting rate to reduce the number of training samples required in the incremental learning process.In order to verify the effectiveness and superiority of the algorithm,the experimental part compared the existing algorithms in the real system data set and the UCI data set with 3 types and 6 groups of imbalanced data sets.The results show that the proposed algorithm can effectively realize the incremental learning of imbalanced data,so as to meet the requirements of the C4ISR state monitoring.
command control communication and computer intelligence surveillance and reconnaissance(C4ISR)system monitoringsupport vector machine(SVM)imbalanced dataincremental learning
万方数据
维普
