一种简单的基于深度q网络的特征选择方法

Albert Budi Christian, Yi-Qi Zhong, Wan-Hsun Hu, Chia-Hsuan Yu, Chih-Yu Lin
{"title":"一种简单的基于深度q网络的特征选择方法","authors":"Albert Budi Christian, Yi-Qi Zhong, Wan-Hsun Hu, Chia-Hsuan Yu, Chih-Yu Lin","doi":"10.1109/APWCS60142.2023.10234067","DOIUrl":null,"url":null,"abstract":"In this paper, a feature selection method based on a Deep Q-Network (DQN), a state generator module as well as a feature selection module is proposed. The state generator module generates a state vector based on the selected feature subset and the summary of dataset. On the other hand, the feature selection module selects features based on the Q-values and a predefined threshold. Our experimental results show that our proposed methods exhibit good performance in terms of training time.","PeriodicalId":375211,"journal":{"name":"2023 VTS Asia Pacific Wireless Communications Symposium (APWCS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Simple Deep Q-Network based Feature Selection Method\",\"authors\":\"Albert Budi Christian, Yi-Qi Zhong, Wan-Hsun Hu, Chia-Hsuan Yu, Chih-Yu Lin\",\"doi\":\"10.1109/APWCS60142.2023.10234067\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a feature selection method based on a Deep Q-Network (DQN), a state generator module as well as a feature selection module is proposed. The state generator module generates a state vector based on the selected feature subset and the summary of dataset. On the other hand, the feature selection module selects features based on the Q-values and a predefined threshold. Our experimental results show that our proposed methods exhibit good performance in terms of training time.\",\"PeriodicalId\":375211,\"journal\":{\"name\":\"2023 VTS Asia Pacific Wireless Communications Symposium (APWCS)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 VTS Asia Pacific Wireless Communications Symposium (APWCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APWCS60142.2023.10234067\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 VTS Asia Pacific Wireless Communications Symposium (APWCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APWCS60142.2023.10234067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文提出了一种基于深度q网络(Deep Q-Network, DQN)、状态生成器模块和特征选择模块的特征选择方法。状态生成器模块根据所选择的特征子集和数据集的摘要生成状态向量。另一方面,特征选择模块根据q值和预定义的阈值选择特征。实验结果表明,本文提出的方法在训练时间方面具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Simple Deep Q-Network based Feature Selection Method
In this paper, a feature selection method based on a Deep Q-Network (DQN), a state generator module as well as a feature selection module is proposed. The state generator module generates a state vector based on the selected feature subset and the summary of dataset. On the other hand, the feature selection module selects features based on the Q-values and a predefined threshold. Our experimental results show that our proposed methods exhibit good performance in terms of training time.
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