基于排序过程的实例选择算法

C. S. Pereira, George D. C. Cavalcanti
{"title":"基于排序过程的实例选择算法","authors":"C. S. Pereira, George D. C. Cavalcanti","doi":"10.1109/IJCNN.2011.6033531","DOIUrl":null,"url":null,"abstract":"This paper presents an innovative instance selection method, called Instance Selection Algorithm based on a Ranking Procedure (ISAR), which is based on a ranking criterion. The ranking procedure aims to order the instances in the data set; better the instance higher the score associate to it. With the purpose of eliminating irrelevant instances, ISAR also uses a coverage strategy. Each instance delimits a hypersphere centered in it. The radius of each hypersphere is used as a normalization factor in the classification rule; bigger the radius smaller the distance. After a comparative study using real-world databases, the ISAR algorithm reached promising generalization performance and impressive reduction rates when compared with state of the art methods.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Instance selection algorithm based on a Ranking Procedure\",\"authors\":\"C. S. Pereira, George D. C. Cavalcanti\",\"doi\":\"10.1109/IJCNN.2011.6033531\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an innovative instance selection method, called Instance Selection Algorithm based on a Ranking Procedure (ISAR), which is based on a ranking criterion. The ranking procedure aims to order the instances in the data set; better the instance higher the score associate to it. With the purpose of eliminating irrelevant instances, ISAR also uses a coverage strategy. Each instance delimits a hypersphere centered in it. The radius of each hypersphere is used as a normalization factor in the classification rule; bigger the radius smaller the distance. After a comparative study using real-world databases, the ISAR algorithm reached promising generalization performance and impressive reduction rates when compared with state of the art methods.\",\"PeriodicalId\":415833,\"journal\":{\"name\":\"The 2011 International Joint Conference on Neural Networks\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2011 International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.2011.6033531\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2011 International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2011.6033531","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

本文提出了一种基于排序准则的实例选择方法,称为基于排序过程的实例选择算法(ISAR)。排序过程的目的是对数据集中的实例进行排序;实例越好,与其关联的分数越高。为了消除不相关的实例,ISAR还使用覆盖策略。每个实例都划定一个以它为中心的超球。将每个超球的半径作为分类规则的归一化因子;半径越大,距离越小。在使用真实世界的数据库进行比较研究后,与最先进的方法相比,ISAR算法达到了很好的泛化性能和令人印象深刻的减少率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Instance selection algorithm based on a Ranking Procedure
This paper presents an innovative instance selection method, called Instance Selection Algorithm based on a Ranking Procedure (ISAR), which is based on a ranking criterion. The ranking procedure aims to order the instances in the data set; better the instance higher the score associate to it. With the purpose of eliminating irrelevant instances, ISAR also uses a coverage strategy. Each instance delimits a hypersphere centered in it. The radius of each hypersphere is used as a normalization factor in the classification rule; bigger the radius smaller the distance. After a comparative study using real-world databases, the ISAR algorithm reached promising generalization performance and impressive reduction rates when compared with state of the art methods.
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:604180095
Book学术官方微信