基于改进随机森林算法的评估指标精简方法研究

doi:10.12305/j.issn.1001-506X.2023.07.21

Abstract

Abstract:

To obtain the effectiveness evaluation results of weapon equipment system quickly and accurately, a method is proposed that can simplify the effectiveness evaluation index of weapon equipment systems. Based on the importance of evaluation index measured by random forest (RF) algorithm, the importance of evaluation index can be sorted in the case of missing index value, and then the specified number of important evaluation index can be refined by improving the decision tree partition attribute selection, leaf node value determination, and decision tree output value calculation methods in RF algorithm. Based on the simulation data set with missing index value, it is verified that the improved RF algorithm is more accurate than the traditional RF algorithm in the application scenario of air defense system effectiveness evaluation index simplification.

Key words: missing index value, evaluation index, simplifying method, random forest (RF)

CLC Number:

TP181

Shaochi CHENG, Guangrong YOU. Research on the method of simplifying evaluation index based on improved random forest algorithm[J]. Systems Engineering and Electronics, 2023, 45(7): 2108-2113.

Figures/Tables 4

References 31

1	BI W H , GAO F , ZHANG A . A novel weapon system effectiveness assessment method based on the interval-valued evidential reasoning algorithm and the analytical hierarchy process[J]. IEEE Access, 2021, 9, 53480- 53490. doi: 10.1109/ACCESS.2021.3071419
2	CHEN H, WANG R H, WANG J J, et al. Weapon system of systems evaluation method based on capability perspective[C]//Proc. of the 2nd International Conference on Applied Machine Learning, 2020: 200-203.
3	QI Z Y , ZHANG Y Y , FANG L Q , et al. Research on effectiveness evaluation method of weapon system based on cloud model[J]. Journal of Physics Conference Series, 2021, 1965 (1): 012005. doi: 10.1088/1742-6596/1965/1/012005
4	CHEN Z W, YANG L, WEI J X, et al. Weapon system effectiveness assessment methodology study[C]//Proc. of the 2nd International Conference on Machine Learning and Computer Application, 2021.
5	SU B , LIU H P , ZHANG G C . Anti-UAV group effectiveness evaluation based on fuzzy analytic hierarchy process[J]. Journal of Physics: Conference Series, 2021, 1802 (2): 022098. doi: 10.1088/1742-6596/1802/2/022098
6	WU H, GAO L, LI M Y. Effectiveness evaluation of army equipment support force in the new situation[C]//Proc. of the 2nd International Conference on Computing and Data Science, 2021.
7	FANG Z , SHUANG W U , ZHANG X , et al. ADC-GERT network parameter estimation model for mission effectiveness of joint operation system[J]. Journal of Systems Engineering and Electronics, 2021, 32 (6): 1394- 1460. doi: 10.23919/JSEE.2021.000119
8	TAN X P , ZHANG Q . Review of system contribution rate eva-luation[J]. Scientific Journal of Intelligent Systems Research, 2022, 4 (5): 310- 317.
9	JIANG J Y, LI J C, XIA B Y, et al. Modeling and resilience analysis of combat systems-of-systems based on kill web[C]//Proc. of the 17th Annual System of Systems Engineering Conference, 2022: 28-35.
10	LIU P , LI J C , XIA B Y , et al. Weapons equipment portfolios selection based on equipment system contribution rates[J]. Journal of Systems Engineering and Electronics, 2021, 32 (3): 584- 595. doi: 10.23919/JSEE.2021.000050
11	HAN Y M , LIU T L , LI Y , et al. Construction of intelligent weapon system effectiveness evaluation index system based on Delphi method[J]. Journal of Physics: Conference Series, 2020, 1570 (1): 012049. doi: 10.1088/1742-6596/1570/1/012049
12	HUANG Q , ZHANG Y S , ZHANG B Z , et al. Emerging SEM equipment system combat capability assessment method[J]. Procedia Computer Science, 2021, 183 (5): 545- 550.
13	LI Q Y , CHEN G M , LI Z Q , et al. Evaluation of missile electromagnetic launch system based on effectiveness[J]. Journal of Intelligent & Fuzzy Systems, 2021, 41 (6): 6517- 6526.
14	乔蓉, 周峰. 基于PCA-BP神经网络地空导弹武器系统作战效能评估[J]. 军事运筹与系统工程, 2020, 34 (4): 38- 43.
	QIAO R , ZHOU F . Operational effectiveness evaluation of surface-to air missile weapon system based on PCA-BP neural network[J]. Military Operations Research and Systems Engineering, 2020, 34 (4): 38- 43.
15	姬正一, 陈阳, 沈培志, 等. 决策树集成方法在反舰导弹效能评估中的应用[J]. 现代防御技术, 2021, 49 (4): 15- 23.
	JI Z Y , CHEN Y , SHEN P Z , et al. Decision tree integration method application in effectiveness evaluation of anti-ship missile weapon system[J]. Modern Defence Technology, 2021, 49 (4): 15- 23.
16	HE Y L, HU F Y, LV L L, et al. Research on effectiveness evaluation of equipment maintenance support based on simulation[C]//Proc. of the International Conference on Intelligent Computing, Automation and Systems, 2019: 339-343.
17	ZHANG X W , LI Y X , ZHAO J F , et al. Review on effectiveness evaluation of missile weapon system[J]. International Journal of Frontiers in Engineering Technology, 2021, 3 (9): 30- 35.
18	DING J F , SI G Y , LI B Q , et al. Construction of composite indicator system based on simulation data mining[J]. Journal of Systems Engineering and Electronics, 2017, 28 (1): 81- 87. doi: 10.21629/JSEE.2017.01.10
19	LI X L, LU J Y, GAO Z B, et al. Effectiveness evaluation of kill chain based on PCA, AHP and entropy weight method[C]// Proc. of the 3rd International Conference on Modelling, Simulation and Applied Mathematics, 2018: 192-196.
20	HE Z X , ZHAO L J , GE L , et al. Comprehensive evaluation of weapon equipment systems operational effectiveness based on state space concept[J]. Journal of Physics: Conference Series, 2005, (1): 012099.
21	牛得清, 伍友利, 徐洋, 等. 红外空空导弹抗干扰效能评估建模[J]. 北京航空航天大学学报, 2021, 47 (9): 1874- 1883.
	NIU D Q , WU Y L , XU Y , et al. Modeling of anti jamming effectiveness evaluation of infrared air-to-air missile[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47 (9): 1874- 1883.
22	BREIMAN L . Random forest[J]. Machine Learning, 2001, 45 (1): 5- 32. doi: 10.1023/A:1010933404324
23	GOLDSTEIN B A , POLLEY E C , BRIGGS F B . Random fo-rests for genetic association studies[J]. Statistical Applications in Genetics and Molecular Biology, 2011, 10 (1): 2- 32.
24	FRIEDMAN J H . Greedy function approximation: a gradient boosting machine[J]. Annals of Statistics, 2000, 29 (5): 1189- 1232.
25	CALLE M L , URREA V . Letter to the editor: stability of random forest importance measures[J]. Briefings in Bioinformatics, 2011, 12 (1): 86- 89. doi: 10.1093/bib/bbq011
26	STROL C , BOULESTEIX A L , ZEILEIS A , et al. Bias in random forest variable importance measures: illustrations, sources and a solution[J]. BMC Bioinformatics, 2007, 8, 25. doi: 10.1186/1471-2105-8-25
27	KRISTIN K , NICODEMU S . Letter to the editor: on the stability and ranking of predictors from random forest variable importance measures[J]. Briefings in Bioinformatics, 2001, 12 (4): 369- 373.
28	QUINLAN J R . Induction of decision trees[J]. Machine Learning, 1986, 1 (1): 81- 106.
29	QUINLAN J R . C4.5: programs for machine learning[M]. San Mateo: Morgan Kaufmann Publishers, 1992.
30	BREIMAN L , FRIEDMAN J H , STONE C J , et al. Classification and regression trees (CART)[M]. Boca Raton: Chapman and Hall/CRC, 1984.
31	杨冬波. 地空导弹武器系统效能评估[D]. 南京: 南京理工大学, 2013.
	YANG D B. Effectiveness evaluation of surface-to-air missile weapon system[D]. Nanjing: Nanjing University of Science and Technology, 2013.

序号	指标							评估结果
序号	1	2	3	4	5	…	18	评估结果
1	7	3	7	3	1	…	7	4
2	2	5	9	4	5	…	9	3
3	8	6	9	5	6	…	3	4
4	6	2	1	6	4	…	5	4
5	6	3	3	2	1	…	8	3

指标值缺失百分比/%	算法	重要性高指标错误个数	重要性中指标错误个数	重要性低指标错误个数	总数
10	传统算法	0	1	1	2
10	改进算法	0	0	0	0
30	传统算法	2	3	1	6
30	改进算法	1	1	0	2
50	传统算法	3	4	1	8
50	改进算法	1	1	0	2

[1]	Zhihai YAO, Jianping LIU, Jianping WANG, Wei LI, Bo REN. Evaluation index system of ground-based TT&C resource scheduling [J]. Systems Engineering and Electronics, 2020, 42(8): 1751-1758.
[2]	Liangliang HAO, Jinsheng ZHANG, Ting LI, Xiaoyu MA. Design of evaluation system for missile simulation training [J]. Systems Engineering and Electronics, 2020, 42(4): 843-850.
[3]	QIAN Yan-ling, LI Ting-peng, LI Lei, DU Kai. Evaluation index framework of maintenance system and its selection method [J]. Systems Engineering and Electronics, 2014, 36(6): 1088-1095.

Research on the method of simplifying evaluation index based on improved random forest algorithm

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 4

References 31

Related Articles 3

Recommended Articles

Metrics

Comments