Case Study: Knowledge Discovery Process using Computation Intelligence with Feature Selection Approach

Khin Sandar Kyaw, S. Limsiroratana
{"title":"Case Study: Knowledge Discovery Process using Computation Intelligence with Feature Selection Approach","authors":"Khin Sandar Kyaw, S. Limsiroratana","doi":"10.1109/ICTKE47035.2019.8966927","DOIUrl":null,"url":null,"abstract":"Since today is the age of data which are presented using electronic documents, knowledge discovery process (KDP) for different types of data is become a popular topic in various application areas for developing automatic systems. Meanwhile, the capacity of computation intelligence (CI) for solving complex problem, for instance complex features, in KDP is also become a critical role in order to provide effective performance and efficient computation time. In this paper, we observed case study about new trend for KDP using CI for the area of text document classification (TDC). According to the experimental results from different cases, CI can enhance the performance of TDC by looking for optimal subset of feature according to the objective function of learning models.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTKE47035.2019.8966927","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Since today is the age of data which are presented using electronic documents, knowledge discovery process (KDP) for different types of data is become a popular topic in various application areas for developing automatic systems. Meanwhile, the capacity of computation intelligence (CI) for solving complex problem, for instance complex features, in KDP is also become a critical role in order to provide effective performance and efficient computation time. In this paper, we observed case study about new trend for KDP using CI for the area of text document classification (TDC). According to the experimental results from different cases, CI can enhance the performance of TDC by looking for optimal subset of feature according to the objective function of learning models.
案例研究:基于特征选择方法的计算智能知识发现过程
由于今天是数据以电子文档形式呈现的时代,针对不同类型数据的知识发现过程(knowledge discovery process, KDP)已成为自动化系统开发中各个应用领域的热门话题。同时,为了提供有效的性能和高效的计算时间,计算智能(CI)在KDP中解决复杂问题(如复杂特征)的能力也变得至关重要。本文对文本文档分类(TDC)领域中使用CI的KDP的新趋势进行了案例研究。根据不同案例的实验结果,CI可以根据学习模型的目标函数寻找最优的特征子集,从而提高TDC的性能。
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