不平衡数据下基于SVM增量学习的指挥信息系统状态监控方法

doi:10.12305/j.issn.1001-506X.2024.03.25

Abstract

Abstract:

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.

Key words: command control communication and computer intelligence surveillance and reconnaissance (C4ISR), system monitoring, support vector machine (SVM), imbalanced data, incremental learning

CLC Number:

TP315

Zhiqiang JIAO, Kan YI, Jieyong ZHANG, Peiyang YAO. C4ISR state monitoring method based on SVM incremental learning of imbalanced data[J]. Systems Engineering and Electronics, 2024, 46(3): 992-1003.

Figures/Tables 17

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References 30

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算法	0	1	2	3	4	5	6	7	8	9	10
SVM	200.0	220.0	240.0	260.0	280.0	300.0	320.0	340.0	360.0	380.0	400.0
SVM+INV	200.0	206.3	226.1	245.9	265.7	285.6	305.5	325.3	345.2	365.2	385.0
SVM+UB	300.0	323.9	347.9	371.7	395.8	419.9	443.9	468.0	492.0	516.0	540.0
本文算法	300.0	289.7	309.2	324.5	339.8	354.8	370.1	385.2	398.6	411.9	424.8

算法	0	1	2	3	4	5	6	7	8	9	10
SVM	0.35	2.99	2.97	2.87	2.90	3.00	3.22	3.37	3.47	3.52	3.54
SVM+INV	0.35	4.34	4.36	4.48	4.51	4.75	4.92	5.10	5.24	5.36	5.54
SVM+UB	8.81	12.90	13.72	14.50	15.69	16.38	17.87	18.77	20.10	21.70	22.82
本文算法	8.88	13.03	13.47	14.16	14.67	15.14	15.29	16.38	16.48	16.97	17.37

数据集	特征数目	样本数量	不平衡比
yeast1	8	1 484	2.06
yeast3	8	1 484	8.1
ecoli1	7	336	3.36
ecoli4	7	336	15.8
poker-8-9vs6	10	1 485	58.4
poker-8-9vs5	10	2 075	82

算法	yeast1	yeast3	ecoli1	ecoli4	poker-8-9vs6	poker-8-9vs5
Simple-ISVM	332.0	175.4	59.1	51.3	99.5	124.8
KKT-ISVM	486.6	207.0	71.7	45.9	56.3	62.0
CRS-ISVM	486.8	206.9	73.8	46.8	61.3	66.4
本文算法	659.5	657.9	119.0	132.5	568.4	797.5

算法	yeast1	yeast3	ecoli1	ecoli4	poker-8-9vs6	poker-8-9vs5
Simple-ISVM	4.44	2.69	1.77	1.78	2.07	2.21
KKT-ISVM	8.36	3.86	1.95	1.88	2.44	2.66
CRS-ISVM	11.16	6.18	2.52	2.48	4.84	5.89
本文算法	36.24	50.38	4.01	5.13	42.42	77.33

C4ISR state monitoring method based on SVM incremental learning of imbalanced data

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