Utilization of Dynamic Reducts to Improve Performance of the Rule-Based Similarity Model for Highly-Dimensional Data

Andrzej Janusz
{"title":"Utilization of Dynamic Reducts to Improve Performance of the Rule-Based Similarity Model for Highly-Dimensional Data","authors":"Andrzej Janusz","doi":"10.1109/WI-IAT.2010.118","DOIUrl":null,"url":null,"abstract":"This paper presents an extension to the Rule-Based Similarity (RBS) model -- a novel rough set approach to the problem of learning a similarity relation from data. The original model, proposed in [1], applied the notion of Tversky's feature contrast model in a rough set framework to facilitate an accurate case-based classification. In the dynamic RBS model, a dynamic reducts technique is used to broaden the scope of the considered similarity aspects. This is especially important when dealing with objects described by numerous attributes. The extended model was tested on several microarray datasets from RSCTC'2010 Discovery Challenge. The results proved that it is significantly more accurate than the original RBS as well as some other popular classification algorithms, such as the \\emph{random forest} or $k$-NN combined with several attribute selection methods.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI-IAT.2010.118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

This paper presents an extension to the Rule-Based Similarity (RBS) model -- a novel rough set approach to the problem of learning a similarity relation from data. The original model, proposed in [1], applied the notion of Tversky's feature contrast model in a rough set framework to facilitate an accurate case-based classification. In the dynamic RBS model, a dynamic reducts technique is used to broaden the scope of the considered similarity aspects. This is especially important when dealing with objects described by numerous attributes. The extended model was tested on several microarray datasets from RSCTC'2010 Discovery Challenge. The results proved that it is significantly more accurate than the original RBS as well as some other popular classification algorithms, such as the \emph{random forest} or $k$-NN combined with several attribute selection methods.
利用动态约简提高基于规则的高维数据相似度模型的性能
本文提出了基于规则的相似性(RBS)模型的扩展——一种新的粗糙集方法,用于从数据中学习相似关系的问题。在[1]中提出的原始模型在粗糙集框架中应用了Tversky的特征对比模型的概念,以促进基于案例的准确分类。在动态RBS模型中,使用动态约简技术来扩大所考虑的相似性方面的范围。在处理由众多属性描述的对象时,这一点尤其重要。扩展模型在RSCTC 2010年发现挑战赛的几个微阵列数据集上进行了测试。结果证明,该方法的准确率明显高于原有的RBS以及其他一些流行的分类算法,如\emph{随机森林}或结合几种属性选择方法的$k$ -NN。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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