规则归纳的协同进化算法

P. Myszkowski
{"title":"规则归纳的协同进化算法","authors":"P. Myszkowski","doi":"10.1109/IMCSIT.2010.5679728","DOIUrl":null,"url":null,"abstract":"This paper describes our last research results in the field of evolutionary algorithms for rule extraction applied to classification (and image annotation). We focus on the data mining classification task and we propose evolutionary algorithm for rule extraction. Presented approach is based on binary classical genetic algorithm with representation of `if-then' rules and we propose two specialized genetic operators. We want to show that some search space reduction techniques make possible to get solution comparable to others from literature. To present our method ability of discovering the set of rules with high F-score we tested our approach on four benchmark datasets and ImageCLEF competition dataset.","PeriodicalId":147803,"journal":{"name":"Proceedings of the International Multiconference on Computer Science and Information Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Coevolutionary algorithm for rule induction\",\"authors\":\"P. Myszkowski\",\"doi\":\"10.1109/IMCSIT.2010.5679728\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes our last research results in the field of evolutionary algorithms for rule extraction applied to classification (and image annotation). We focus on the data mining classification task and we propose evolutionary algorithm for rule extraction. Presented approach is based on binary classical genetic algorithm with representation of `if-then' rules and we propose two specialized genetic operators. We want to show that some search space reduction techniques make possible to get solution comparable to others from literature. To present our method ability of discovering the set of rules with high F-score we tested our approach on four benchmark datasets and ImageCLEF competition dataset.\",\"PeriodicalId\":147803,\"journal\":{\"name\":\"Proceedings of the International Multiconference on Computer Science and Information Technology\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the International Multiconference on Computer Science and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMCSIT.2010.5679728\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Multiconference on Computer Science and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMCSIT.2010.5679728","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

本文描述了我们在应用于分类(和图像注释)的规则提取进化算法领域的最新研究成果。重点研究了数据挖掘分类任务,提出了基于进化算法的规则提取方法。提出了一种基于二元经典遗传算法的“if-then”规则表示方法,并提出了两个专门的遗传算子。我们希望展示一些搜索空间缩减技术可以得到与文献中其他解决方案相当的解决方案。为了展示我们的方法发现高f分规则集的能力,我们在四个基准数据集和ImageCLEF竞争数据集上测试了我们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Coevolutionary algorithm for rule induction
This paper describes our last research results in the field of evolutionary algorithms for rule extraction applied to classification (and image annotation). We focus on the data mining classification task and we propose evolutionary algorithm for rule extraction. Presented approach is based on binary classical genetic algorithm with representation of `if-then' rules and we propose two specialized genetic operators. We want to show that some search space reduction techniques make possible to get solution comparable to others from literature. To present our method ability of discovering the set of rules with high F-score we tested our approach on four benchmark datasets and ImageCLEF competition dataset.
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