基于近似粗糙集分辨力分类算法的飞机类型预测

国强; 赵佳琦

doi:10.13224/j.cnki.jasp.20210304

基于近似粗糙集分辨力分类算法的飞机类型预测

doi: 10.13224/j.cnki.jasp.20210304

国强^{1, 2,},
赵佳琦^1,*, ,

1.
中国航空无线电电子研究所航空电子系统综合技术重点实验室，上海 200040
2.
哈尔滨工程大学信息与通信工程学院，哈尔滨 150001

基金项目: 航空科学基金（202000550P6001）

详细信息

作者简介:
国强（1972-），男，教授，博士，主要从事雷达通信抗干扰及电子对抗的研究。E-mail：guoqiang@hrbeu.edu.cn

通讯作者:
赵佳琦（1997-），女，硕士，主要从事粗糙集理论研究及飞机类型预测的研究。E-mail：zhaojq97@hrbeu.edu.cn

中图分类号: V247；TP273
计量
- 文章访问数: 215
- HTML浏览量: 26
- PDF量: 342
- 被引次数: 0
出版历程
- 收稿日期: 2021-06-16
- 网络出版日期: 2023-04-03

Aircraft type prediction based on approximate rough set resolution classification algorithm

GUO Qiang^{1, 2
,},
ZHAO Jiaqi^{1,*
, ,}

1.
Science and Technology on Avionicsntegration Laboratory， China Aviation Radio Electronics Research Institute，Shanghai 200040，China
2.
College of Information and Communication Engineering， Harbin Engineering University，Harbin 150001，China

摘要

摘要:
传统的C4.5分类决策树作为数据分类算法具有计算简单、准确率高的优势，由于飞机具有参数多和数据量大的因素，C4.5算法需要对连续属性值进行多次顺序扫描，分类时间效率较低。针对此问题，提出近似粗糙集和决策分辨力分类算法，利用粗糙集近似度来判断属性划分样本数据能力，并将其代入到决策分辨力算法中，以决策分辨力最大的属性作为分裂特征建立分类决策树。算法在保证分类决策准确率的同时，提高计算效率并减少过拟合问题的产生。通过对UCI（University of California, Irvine）数据集上多组数据样本的对比实验分析，验证了本文提出PSRP（rough set and resolving power）的算法在保证相同准确率的情况下，平均计算时间效率提升约10%，可靠性提升2%。
- 近似粗糙集 /
- 决策分辨力 /
- 分类决策树 /
- 数据融合 /
- 飞机类型预测
Abstract:
As a traditional data classification algorithm, C4.5 Decision Tree has the advantages of simple calculation and high accuracy. Due to the fact that aircraft has many parameters and large amount of data, C4.5 Decision Tree requires multiple sequential scanning of continuous attribute values, but the classification time efficiency is low. To solve this problem, an approximate rough set resolution classification algorithm was proposed. Rough set approximation was used to determine the ability of attributes to divide sample data. According to the probability acquired, the attribute with the largest resolution was used as the split character to establish the classification decision tree. The algorithm improved the computational efficiency and reduced the generation of over-fitting problem while ensuring the accuracy of classification decision. Through comparative experimental analysis of multiple sets of data samples on UCI (University of California, Irvine) databases, it proved that the PSRP (rough set and resolving power) algorithm improved the average computational time efficiency by about 10% and reliability by 2% while ensuring the same accuracy.
- approximate rough set /
- decision resolution power /
- classification decision tree /
- data fusion /
- aircraft type prediction

HTML

图 1 近似粗糙集正确率，错误率和未识别率

Figure 1. Approximate rough set accuracy, error rate and unrecognized rate

下载: 全尺寸图片幻灯片

图 2 RSRP算法流程图

Figure 2. RSRP algorithm flow chart

下载: 全尺寸图片幻灯片

图 3 批发客户数据集的近似度取值

Figure 3. Approximation value of data set wholesale customer

下载: 全尺寸图片幻灯片

图 4 心力衰竭数据集的近似度取值

Figure 4. Approximation value of data set heart failure

下载: 全尺寸图片幻灯片

图 5 飞机类型预测近似度取值

Figure 5. Approximation value of aircraft type prediction

下载: 全尺寸图片幻灯片

表 1 数据集描述

Table 1. Data set description

序号	数据集名称	样本个数	条件属性	决策类
1	heart failure	299	12	2
2	hcvdat	615	12	5
3	user model	258	5	4
4	liver patient	583	10	2
5	ionosphere	351	33	2
6	exasens	399	3	4
7	transfusion	748	4	2
8	wholesale customer	440	7	2
9	messidor	1151	19	2
10	spy plane finder	597	31	2

下载: 导出CSV

表 2 数据集分类评估指标对比

Table 2. Comparison of evaluation indexes for data set classification

数据集名称	F₁			时间效率			树形复杂度
数据集名称	C4.5	RS-CART	RSRP	C4.5	RS-CART	RSRP	C4.5	RS-CART	RSRP
heart failure	0.7997	0.7715	0.8152	0.2829	0.1862	0.2421	6.78	4.42	3.98
hcvdat	0.9084	0.8616	0.8925	1.8430	1.0443	1.0243	7.90	4.82	4.00
user model	0.8954	0.8292	0.9065	0.2044	0.1767	0.1913	5.56	4.54	4.24
liver patient	0.6693	0.6942	0.7020	1.6517	0.8661	0.6875	44.09	6.44	6.39
ionosphere	0.8823	0.8920	0.8964	1.9100	1.0584	1.1292	4.52	3.64	3.46
exasens	0.5188	0.5214	0.5416	0.0789	0.0662	0.0734	8.02	6.12	6.54
transfusion	0.7591	0.7775	0.7695	0.2505	0.1905	0.1736	17.54	7.96	5.02
wholesale customer	0.8895	0.8996	0.9065	0.8743	0.6317	0.6417	8.62	5.20	4.40
messidor	0.5924	0.5551	0.6410	39.2418	7.7538	6.8217	104.2	10.24	6.6
平均值	0.7683	0.7558	0.7857	5.1486	1.3304	1.2205	23.0256	5.9311	4.9589

下载: 导出CSV

表 3 飞机类型预测评估指标对比

Table 3. Comparison of aircraft type prediction evaluation indexes

评价指标	C4.5	RS-CART	RSRP
F₁	0.8710	0.8655	0.8744
时间效率	6.0691	5.5738	4.7178
树形复杂度	7.64	4.58	4.94

下载: 导出CSV

参考文献(28)

[1]	COLUCCIA A,PARISI G,FASCISTA A. Detection and classification of multirotor drones in radar sensor networks: a review[J]. Sensors,2020,20(15): 1-22. doi: 10.1109/JSEN.2020.3003972
[2]	LEI P,WANG J,GUO P,et al. Automatic classification of radar targets with micro-motions using entropy segmentation and time-frequency features[J]. Electron Commun,2011,65(10): 806-813.
[3]	ZHOU G Q,ZHANG R T,ZHANG D J. Manifold learning co-location decision tree for remotely sensed imagery classification[J]. Remote Sens,2016,8(855): 1-31.
[4]	同亚龙,王彤,代保全,等. 一种抗密集欺骗式干扰的机载雷达动目标检测方法[J]. 电子与信息学报,2015,37(3): 658-664. doi: 10.11999/JEIT140679 TONG Yalong,WANG Tong,DAI Baoquan,et al. A moving target detection method to suppress dense deception jamming for airborne radar[J]. Journal of Electronics & Information Technology,2015,37(3): 658-664. (in Chinese) doi: 10.11999/JEIT140679
[5]	王福友,罗钉,刘宏伟. 低分辨机载雷达飞机目标分类识别技术研究[J]. 雷达学报,2014,3(4): 444-449. WANG Fuyou,LUO Ding,LIU Hongwei. Low-resolution airborne radar aircraft target classification[J]. Journal of Radars,2014,3(4): 444-449. (in Chinese)
[6]	雷涛,旷生玉,杨玲. 基于深度学习的雷达辐射源型号识别方法[J]. 电子信息对抗技术,2019,34(4): 29-34. doi: 10.3969/j.issn.1674-2230.2019.04.006 LEI Tao,KUANG Shengyu,YANG Ling. Radar emitter type identification algorithm based on deep learning[J]. Electronic Information Warfare Technology,2019,34(4): 29-34. (in Chinese) doi: 10.3969/j.issn.1674-2230.2019.04.006
[7]	欧阳瑞麒,雍杨,王兵学. 卷积神经网络在飞机类型识别中的应用[J]. 兵工自动化,2017,36(12): 71-75. OUYANG Ruilin,YONG Yang,WANG Bingxue. Application of convolution neural network in aircraft type recognition[J]. Ordnance Industry Automation,2017,36(12): 71-75. (in Chinese)
[8]	刘相云,龚志辉,金飞,等. 结合显著图和深度学习的遥感影像飞机目标识别[J]. 测绘通报,2019(3): 27-31. LIU Xiangyun,GONG Zhihui,JIN Fei,et al. Aircraft target recognition in remote sensing image combined saliency map with deep learning[J]. Bulletin of Surveying and Mapping,2019(3): 27-31. (in Chinese)
[9]	ZHANG K,ZHAN J M,WU W Z. Novel fuzzy rough set models and corresponding applications to multi- criteria decision-making[J]. Fuzzy Sets and Systems,2020(383): 92-126.
[10]	YAO Y Y,SHE Y H. Rough set models in multigranulation spaces[J]. Information Sciences,2016(327): 40-56.
[11]	王越,万洪. 一种新的应用变精度粗糙集的决策树构造方法[J]. 重庆理工大学学报(自然科学),2013,27(11): 58-64. WANG Yue,WAN Hong. A new method for constructing decision tree based on variable precision rough set[J]. Journal of Chongqing University of Technology (Natural Science),2013,27(11): 58-64. (in Chinese)
[12]	刘军. 基于叶枝比率的决策树构建算法[J]. 信息网络安全,2013(2): 9-12. LIU Jun. Algorithm of constructing decision tree based on the leaf and branch ratio[J]. Netinfo Security,2013(2): 9-12. (in Chinese)
[13]	JIANG J F,ZHU X Y,HAN G J,et al. A dynamic trust evaluation and update mechanism based on C4.5 decision tree in underwater wireless sensor networks[J]. IEEE Transactions on Vehicular Technology,2020,69(8): 31-40.
[14]	尚朝轩,王品,韩壮志,等. 基于类决策树分类的特征层融合识别算法[J]. 控制与决策,2016,31(6): 1009-1014. SHANG Chaoxuan,WANG Pin,HAN Zhuangzhi,et al. Feature-level fusion recognition algorithm based on analogy decision tree classification[J]. Control and Decision,2016,31(6): 1009-1014. (in Chinese)
[15]	XIAO F Y. Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy[J]. Information Fusion,2019(46): 23-32.
[16]	陈泽华,张裕,谢刚. 不一致决策表规则获取的粒计算方法[J]. 控制与决策,2015,30(4): 709-714. CHEN Zehua,ZHANG Yu,XIE Gang. GrC method of rule acquisition for inconsistent decision table[J]. Control and Decision,2015,30(4): 709-714. (in Chinese)
[17]	LI J H,REN Y,MEI C L,et al. A comparative study of multigranulation rough sets and concept lattices via rule acquisition[J]. Knowledge-Based Systems,2016,91: 152-164. doi: 10.1016/j.knosys.2015.07.024
[18]	孙长嵩,董西国,张健沛. 一个基于粗糙集和决策树的最简分类规则集生成算法[J]. 哈尔滨工程大学学报,2002,23(5): 87-91. SUN Changsong,DONG Xiguo,ZHANG Jianpei. Algorithm based on rough set and decision tree to gain minimal classing rule set[J]. Journal of Harbin Engineering University,2002,23(5): 87-91. (in Chinese)
[19]	王礼沅,张恒喜,徐浩军. 基于粗糙集的空战效能多指标综合评估模型[J]. 航空学报,2008,29(4): 880-885. doi: 10.3321/j.issn:1000-6893.2008.04.018 WANG Liruan,ZHANG Hengxi,XU Haojun. Multi-index synthesize evaluation model based on rough set theory for air combat efficiency[J]. Acta Aeronautica et Astronautica Sinica,2008,29(4): 880-885. (in Chinese) doi: 10.3321/j.issn:1000-6893.2008.04.018
[20]	JIAN L R, LI M Y. A variable precision fuzzy rough set approach to a fuzzy-rough decision table[J]. Science and Engineering Research Center, 2016: 250-254.
[21]	张清华,薛玉斌,王国胤. 粗糙集的最优近似集[J]. 软件学报,2016,27(2): 295-308. ZHANG Qinghua,XUE Yubin,WANG Guoyin. Optimal approximation sets of rough sets[J]. Journal of Software,2016,27(2): 295-308. (in Chinese)
[22]	ZHANG X,MEI C L,CHEN D G,et al. Feature selection in mixed data: a method using a novel fuzzy rough set-based information entropy[J]. Pattern Recognition,2016(56): 1-15.
[23]	ZHAN J M,ALI M I,MEHMOOD N. On a novel uncertain soft set model: Z-soft fuzzy rough set model and corresponding decision making methods[J]. Applied Soft Computing,2017(56): 446-457.
[24]	ZHAN J M,LIU Q,HERAWAN T. A novel soft rough set: soft rough hemirings and corresponding multicriteria group decision making[J]. Applied Soft Computing,2017(54): 393-402.
[25]	张铁柱,蒋宏. 机载雷达和红外数据融合的智能目标识别[J]. 红外与激光工程,2010,39(4): 756-760. doi: 10.3969/j.issn.1007-2276.2010.04.037 ZHANG Tiezhu,JIANG Hong. Intelligent target recognitionbased on the data fusion of radar and infrared imaging sensor[J]. Infrared and Laser Engineering,2010,39(4): 756-760. (in Chinese) doi: 10.3969/j.issn.1007-2276.2010.04.037
[26]	李雪岩,李学伟,蒋君. 基于知识粒度特征的多目标粗糙集属性约简算法[J]. 控制与决策,2021,36(1): 196-205. LI Xueyan,LI Xuewei,JIANG Jun. Multi objective rough set attribute reduction algorithm based on the characteristics of knowledge granularity[J]. Control and Decision,2021,36(1): 196-205. (in Chinese)
[27]	BINH T P,ABOLFAZL J,INDRA P. A novel hybrid support vector machine with decision tree for data classification[J]. International Journal of Advanced and Applied Sciences,2017,4(9): 138-143. doi: 10.21833/ijaas.2017.09.019
[28]	庞梦洋,索中英,郑万泽,等. 基于RS-CART决策树的航空发动机小样本故障诊断[J]. 航空动力学报,2020,35(7): 1559-1568. PANG Mengyang,SUO Zhongying,ZHENG Wanze,et al. Small sample fault diagnosis of aeroengine based on RS-CART decision tree[J]. Journal of Aerospace Power,2020,35(7): 1559-1568. (in Chinese)