基于不精确观测的模糊贝叶斯网络参数学习

M. G. Ahsaee, Mahmoud Naghibzadeh, B. S. Gildeh
{"title":"基于不精确观测的模糊贝叶斯网络参数学习","authors":"M. G. Ahsaee, Mahmoud Naghibzadeh, B. S. Gildeh","doi":"10.3233/KES-140296","DOIUrl":null,"url":null,"abstract":"In recent years, Bayesian Network has become an important modeling method for decision making problems of real-world applications. In this paper learning parameters of a fuzzy Bayesian Network (BN) based on imprecise/fuzzy observations is considered, where imprecise observations particularly refers to triangular fuzzy numbers. To achieve this, an extension to fuzzy probability theory based on imprecise observations is proposed which employs both the \"truth\" concept of Yager and the Extension Principle in fuzzy set theory. In addition, some examples are given to demonstrate the concepts of the proposed idea. The aim of our suggestion is to be able to estimate joint fuzzy probability and the conditional probability tables (CPTs) of Bayesian Network based on imprecise observations. Two real-world datasets, Car Evaluation Database (CED) and Extending Credibility (EC), are employed where some of attributes have crisp (exact) and some of them have fuzzy observations. Estimated parameters of the CED's corresponding network, using our extension, are shown in tables. Then, using Kullback-Leibler divergence, two scenarios are considered to show that fuzzy parameters preserve more knowledge than that of crisp parameters. This phenomenon is also true in cases where there are a small number of observations. Finally, to examine a network with fuzzy parameters versus the network with crisp parameters, accuracy result of predictions is provided which shows improvements in the predictions.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Learning parameters of fuzzy Bayesian Network based on imprecise observations\",\"authors\":\"M. G. Ahsaee, Mahmoud Naghibzadeh, B. S. Gildeh\",\"doi\":\"10.3233/KES-140296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, Bayesian Network has become an important modeling method for decision making problems of real-world applications. In this paper learning parameters of a fuzzy Bayesian Network (BN) based on imprecise/fuzzy observations is considered, where imprecise observations particularly refers to triangular fuzzy numbers. To achieve this, an extension to fuzzy probability theory based on imprecise observations is proposed which employs both the \\\"truth\\\" concept of Yager and the Extension Principle in fuzzy set theory. In addition, some examples are given to demonstrate the concepts of the proposed idea. The aim of our suggestion is to be able to estimate joint fuzzy probability and the conditional probability tables (CPTs) of Bayesian Network based on imprecise observations. Two real-world datasets, Car Evaluation Database (CED) and Extending Credibility (EC), are employed where some of attributes have crisp (exact) and some of them have fuzzy observations. Estimated parameters of the CED's corresponding network, using our extension, are shown in tables. Then, using Kullback-Leibler divergence, two scenarios are considered to show that fuzzy parameters preserve more knowledge than that of crisp parameters. This phenomenon is also true in cases where there are a small number of observations. Finally, to examine a network with fuzzy parameters versus the network with crisp parameters, accuracy result of predictions is provided which shows improvements in the predictions.\",\"PeriodicalId\":210048,\"journal\":{\"name\":\"Int. J. Knowl. Based Intell. Eng. Syst.\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Knowl. Based Intell. Eng. Syst.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/KES-140296\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Knowl. Based Intell. Eng. Syst.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/KES-140296","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

近年来,贝叶斯网络已成为现实应用中决策问题的重要建模方法。本文考虑基于不精确/模糊观测值的模糊贝叶斯网络(BN)的学习参数,其中不精确观测值特指三角模糊数。为此,利用Yager的真值概念和模糊集理论中的可拓原理,提出了一种基于不精确观测的模糊概率论的扩展。此外,还给出了一些例子来说明所提出的思想的概念。我们的建议的目的是能够估计联合模糊概率和贝叶斯网络的条件概率表(cts)基于不精确的观测。使用两个真实世界的数据集,汽车评估数据库(CED)和扩展可信度(EC),其中一些属性具有清晰(精确),而其中一些具有模糊观察值。使用我们的扩展,CED相应网络的估计参数如表所示。然后,利用Kullback-Leibler散度,考虑了两种情况,表明模糊参数比清晰参数保留了更多的知识。这种现象在有少量观察的情况下也是正确的。最后,将模糊参数网络与清晰参数网络进行对比,给出了预测的准确性结果,表明预测的准确性有所提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Learning parameters of fuzzy Bayesian Network based on imprecise observations
In recent years, Bayesian Network has become an important modeling method for decision making problems of real-world applications. In this paper learning parameters of a fuzzy Bayesian Network (BN) based on imprecise/fuzzy observations is considered, where imprecise observations particularly refers to triangular fuzzy numbers. To achieve this, an extension to fuzzy probability theory based on imprecise observations is proposed which employs both the "truth" concept of Yager and the Extension Principle in fuzzy set theory. In addition, some examples are given to demonstrate the concepts of the proposed idea. The aim of our suggestion is to be able to estimate joint fuzzy probability and the conditional probability tables (CPTs) of Bayesian Network based on imprecise observations. Two real-world datasets, Car Evaluation Database (CED) and Extending Credibility (EC), are employed where some of attributes have crisp (exact) and some of them have fuzzy observations. Estimated parameters of the CED's corresponding network, using our extension, are shown in tables. Then, using Kullback-Leibler divergence, two scenarios are considered to show that fuzzy parameters preserve more knowledge than that of crisp parameters. This phenomenon is also true in cases where there are a small number of observations. Finally, to examine a network with fuzzy parameters versus the network with crisp parameters, accuracy result of predictions is provided which shows improvements in the predictions.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
DICO: Dingo coot optimization-based ZF net for pansharpening Hybrid modified weighted water cycle algorithm and Deep Analytic Network for forecasting and trend detection of forex market indices Autonomous gesture recognition using multi-layer LSTM networks and laban movement analysis KinRob: An ontology based robot for solving kinematic problems Machine learning approach for corona virus disease extrapolation: A case study
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1