基于覆盖粗糙Vague软专家集的动物疾病诊断算法

doi:10.16180/j.cnki.issn1007-7820.2021.06.001

摘要/Abstract

摘要：

针对现有Vague软集扩展模型的不足,文中将覆盖粗糙集、Vague软集和软专家集3种模型融合扩展,提出了新的处理不确定性问题的数学模型,即覆盖粗糙Vague软专家集,并研究了相关性质。在此基础上,文中给出了一个基于覆盖粗糙Vague软专家集的动物疾病辅助诊断算法。该算法计算了覆盖粗糙Vague软专家集的上下近似算子,通过隶属函数建立了患病程度和疾病水平的关系,并做出辅助诊断。非洲猪瘟疾病的诊断实验表明,文中所提出的ADADA_CRVSES算法是一种有效的针对动物疾病的辅助诊断算法,疾病诊断准确率达90%以上。

关键词: Vague集, 软集, Vague软集, 软专家集, 覆盖粗糙Vague集, 覆盖粗糙Vague软专家集, 上下近似算子, 疾病诊断算法

Abstract:

In view of the shortcomings of the existing Vague soft set extension model, three models including covering rough set, Vague soft set and soft expert set are merged and expanded, and a new mathematical model for dealing with uncertain problems is proposed in this study, namely covering rough Vague soft expert set, and related properties are studied. On this basis, this study presents an assisted diagnosis algorithm for animal diseases based on a rough set of Vague soft experts. The algorithm calculates the upper and lower approximation operators covering the rough Vague soft expert set, establishes the relationship between the disease degree and the disease level through the membership function, and makes an auxiliary diagnosis. The diagnostic results of African swine fever disease show that the ADADA_CRVSES algorithm proposed in the study is an effective auxiliary diagnosis algorithm for animal diseases, and the accuracy rate of disease diagnosis is more than 90%.

Key words: Vague set, soft set, Vague soft set, soft expert set, covering rough Vague set, covering rough Vague soft expert set, upper and lower approximation operator, disease diagnosis algorithm

中图分类号:

TP182

陈鹏岗,冯晓毅. 基于覆盖粗糙Vague软专家集的动物疾病诊断算法[J]. 电子科技, 2021, 34(6): 1-10.

CHEN Penggang,FENG Xiaoyi. Animal Disease Diagnosis Algorithm Based on Covering Rough Vague Soft Expert Set[J]. Electronic Science and Technology, 2021, 34(6): 1-10.

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参考文献 20

[1]	Gau W L, Buehrer D J. Vague sets[J]. IEEE Transactions on Systems,Man and Cybmetics, 1993,23(2):610-614.
[2]	Molodtsovd D. Soft set theory-first results[J]. Computer and Mathematics with Applications, 1999,37(4):19-31.
[3]	Xu W, Ma J, Wang S Y, et al. Vague soft sets and their properties[J]. Computers & Mathematics with Applications, 2010,59(2):787-794. doi: 10.1016/j.camwa.2009.10.015
[4]	滕艳辉, 王昌. 多目标模糊决策的Vague软集法[J]. 计算机工程与应用, 2012,48(10):6-8.
	Teng Yanhui, Wang Chang. Multicriteria fuzzy decision-making method based on vague soft sets[J]. Computer Engineering and Application, 2012,48(10):6-8.
[5]	王昌. Vague软集的相似度量及其应用[J]. 统计与决策, 2012(2):77-79.
	Wang Chang. Similarity measure of vague soft sets and its application[J]. Statistics & Decision, 2012(2):77-79.
[6]	陈文, 余本功. 基于Vague软集的TOPSIS方法及其应用研究[J]. 计算机工程与应用, 2014,50(17):214-218.
	Chen Wen, Yu Bengong. Theory and application study on modified TOPSIS method based on vague soft sets[J]. Computer Engineering and Applications, 2014,50(17):214-218.
[7]	韦波, 邹瑶, 解青青. 一种Vague软集的模糊综合评判模型[J]. 测绘科学, 2019,44(3):95-100.
	Wei Bo, Zou Yao, Xie Qingqing. Fuzzy comprehensive evaluation model based on vague soft sets[J]. Science of Surveying and Mapping, 2019,44(3):95-100.
[8]	刘庆, 王昌. 基于Vague软集相似度量的快速估算模型[J]. 河北大学学报(自然科学版), 2014,34(5):460-466.
	Liu Qing, Wang Chang. Fast estimation model based on similarity measures of Vague soft sets[J]. Journal of Hebei University(Natural Science Edition), 2014,34(5):460-466.
[9]	陈文, 余本功. 基于Vague软集的模糊群决策方法研究[J]. 计算机工程与应用, 2014,50(7):104-107.
	Chen Wen, Yu Bengong. Research on fuzzy group decision making method based on Vague soft set[J]. Computer Engineering and Applications, 2014,50(7):104-107.
[10]	刘庆, 王昌. 基于Vague软集的投资决策方案优选方法研究[J]. 科技通报, 2015,31(1):4-8.
	Liu Qing, Wang Chang. Research on optimized method of investment decision program based on Vague soft sets[J]. Bulletin of Science and Technology, 2015,31(1):4-8.
[11]	王伟, 武君胜, 朱志祥, 等. 基于Vague软集相似度量的网络舆情综合评判方法[J]. 西北工业大学学报, 2018,36(2):368-374.
	Wang Wei, Wu Junsheng, Zhu Zhixiang, et al. A comprehensive evaluation method of network public opinion based on similarity measure between Vague soft sets[J]. Journal of Northwest Polytechnical University, 2018,36(2):368-374.
[12]	Alkhazaleh S, Salleh A R. Soft expert sets[J]. Advances in Decision Sciences, 2011(1):757868.1-757868.12.
[13]	王伟, 彭进业, 李展. 一种覆盖粗糙Vague集模型及其不确定性度量[J]. 计算机科学, 2012,39(8):228-232.
	Wang Wei, Peng Jinye, Li Zhan. Covering rough Vague sets and uncertainty measurement[J]. Computer Science, 2012,39(8):228-232.
[14]	Hassan N, Alhazaymeh K. Vague soft expert set theory[C]. Putrajaya:The Twentieth National Symposium on Mathematical Sciences, 2012.
[15]	Alhazaymeh K, Hassan N. Generalized Vague soft expert set theory[C]. Selangor:Proceeding of the Universiti Kebangsaan Malaysia,Faculty of Science and Technology Postgraduate Colloquium, 2013.
[16]	Alhazaymeh K, Hassan N. Mapping on generalized vague soft expert set[J]. International Journal of Pure and Applied Mathematics, 2014,93(3):369-376.
[17]	Al-Quran A, Hassan N. Neutrosophic vague soft expert set theory[J]. Journal of Intelligent & Fuzzy Systems, 2016,30(6):3691-3702.
[18]	Selvachandran G, John S J. Possibility interval-valued vague soft expert sets and its similarity measure[J]. International Journal of Fuzzy System Applications, 2017,6(1):108-121. doi: 10.4018/IJFSA
[19]	Bai X P, Zhao H Y. Reliability evaluation in construction quality based on complex vague soft expert set method[J]. Civil Engineering Journal, 2018(33):411-424.
[20]	陈鹏岗, 冯晓毅, 毛晓菲. 覆盖空间下粗糙集和Vague软集融合方法研究[J]. 西北工业大学学报, 2016,34(4):642-649.
	Chen Penggang, Feng Xiaoyi, Mao Xiaofei. Integrating of rough sets and Vague soft sets theory in covering approximation space[J]. Journal of Northwest Polytechnical University, 2016,34(4):642-649.

诊断步骤	覆盖粗糙集	Vague集	软集	软专家集
症状数据的采集		需要充分考虑症状数据的“支持”、“反对”、“弃权”3个方面的决策因素	需要考虑诊断数据对象本身的参数特征
数据的预处理	对病例数据进行随机划分,考虑一般性
专家的综合评价(会诊)				需要考虑专家意见集的影响因素
对疾病作出诊断	对病例数据进行随机划分,考虑一般性	需要充分考虑症状数据的“支持”、“反对”、“弃权”3个方面的决策因素	需要考虑诊断数据对象本身的参数特征	需要考虑专家意见集的影响因素

隶属度	high	middle	low
重	[0.07,0.11]	[0.03,0.14]	[0.08,0.10]
一般	[0.04,0.08]	[0.08,0.10]	[0.08,0.09]
轻	[0.04,0.09]	[0.08,0.11]	[0.08,0.10]

隶属度	high	middle	low
X₁	[0.07,0.11]	[0.03,0.14]	[0.08,0.10]
X₂	[0.07,0.08]	[0.08,0.14]	[0.08,0.09]
X₃	[0.07,0.08]	[0.08,0.10]	[0.08,0.09]
X₄	[0.04,0.08]	[0.08,0.10]	[0.08,0.09]
X₅	[0.04,0.08]	[0.08,0.10]	[0.08,0.09]

算法	胃肠卡他	胃炎	腹膜炎	肠移位	肠痉挛	肠梗阻
ADADA_CRVS	6%	85%	75%	60%	73%	85%
ADADA_CRVSS	9%	91%	95%	90%	90%	95%
ADADA_CRVSES	10%	95%	90%	90%	92%	100%