基于粗糙集的改进属性约简算法

X. Yang, Jiancheng Wan, Ling Zhang
{"title":"基于粗糙集的改进属性约简算法","authors":"X. Yang, Jiancheng Wan, Ling Zhang","doi":"10.1109/SNPD.2007.181","DOIUrl":null,"url":null,"abstract":"An improved heuristic attribute reduction algorithm based on the attribute frequency is presented. After analyzing many other attribute reduction algorithms, we utilize the discernibility matrix and the appeared attribute frequencies to determine each attribute's significance, based on the principle of maximum attribute frequency, we achieved the reduction of the information system. An illustrative example demonstrate the algorithm's effectiveness and validity.","PeriodicalId":197058,"journal":{"name":"Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"An Improved Attribute Reduction Algorithm Based on Rough Set\",\"authors\":\"X. Yang, Jiancheng Wan, Ling Zhang\",\"doi\":\"10.1109/SNPD.2007.181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An improved heuristic attribute reduction algorithm based on the attribute frequency is presented. After analyzing many other attribute reduction algorithms, we utilize the discernibility matrix and the appeared attribute frequencies to determine each attribute's significance, based on the principle of maximum attribute frequency, we achieved the reduction of the information system. An illustrative example demonstrate the algorithm's effectiveness and validity.\",\"PeriodicalId\":197058,\"journal\":{\"name\":\"Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SNPD.2007.181\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNPD.2007.181","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

提出了一种改进的基于属性频率的启发式属性约简算法。在分析了许多其他属性约简算法的基础上,利用可分辨矩阵和出现的属性频率来确定每个属性的重要度,基于属性频率最大的原则,实现了信息系统的约简。算例验证了该算法的有效性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Improved Attribute Reduction Algorithm Based on Rough Set
An improved heuristic attribute reduction algorithm based on the attribute frequency is presented. After analyzing many other attribute reduction algorithms, we utilize the discernibility matrix and the appeared attribute frequencies to determine each attribute's significance, based on the principle of maximum attribute frequency, we achieved the reduction of the information system. An illustrative example demonstrate the algorithm's effectiveness and validity.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信