General Quasi Overlap Functions and Fuzzy Neighborhood Systems-Based Fuzzy Rough Sets With Their Applications

IF 8.9 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-10-04 DOI:10.1109/TKDE.2024.3474728

Mengyuan Li;Xiaohong Zhang;Jiaoyan Shang;Yingcang Ma

{"title":"General Quasi Overlap Functions and Fuzzy Neighborhood Systems-Based Fuzzy Rough Sets With Their Applications","authors":"Mengyuan Li;Xiaohong Zhang;Jiaoyan Shang;Yingcang Ma","doi":"10.1109/TKDE.2024.3474728","DOIUrl":null,"url":null,"abstract":"Fuzzy rough sets are important mathematical tool for processing data using existing knowledge. Fuzzy rough sets have been widely studied and used into various fields, such as data reduction and image processing, etc. In extensive literature we have studied, general quasi overlap functions and fuzzy neighborhood systems are broader than other all fuzzy operators and knowledge used in existing fuzzy rough sets, respectively. In this article, a novel fuzzy rough sets model (shortly (\n<italic>I\n, \n<italic>Q\n, \n<italic>NS\n)-fuzzy rough sets) is proposed using fuzzy implications, general quasi overlap functions and fuzzy neighborhood systems, which contains almost all existing fuzzy rough sets. Then, a novel feature selection algorithm (called IQNS-FS algorithm) is proposed and implemented using (\n<italic>I\n, \n<italic>Q\n, \n<italic>NS\n)-fuzzy rough sets, dependency and specificity measure. The results of 12 datasets indicate that IQNS-FS algorithm performs better than others. Finally, we input the results of IQNS-FS algorithm into single hidden layer neural networks and other classification algorithms, the results illustrate that the IQNS-FS algorithm can be better connected with neural networks than other classification algorithms. The high classification accuracy of single hidden layer neural networks (a very simple structure) further shows that the attributes selected by the IQNS-FS algorithm are important which can express the features of the datasets.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"8349-8361"},"PeriodicalIF":8.9000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Knowledge and Data Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10705907/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

Fuzzy rough sets are important mathematical tool for processing data using existing knowledge. Fuzzy rough sets have been widely studied and used into various fields, such as data reduction and image processing, etc. In extensive literature we have studied, general quasi overlap functions and fuzzy neighborhood systems are broader than other all fuzzy operators and knowledge used in existing fuzzy rough sets, respectively. In this article, a novel fuzzy rough sets model (shortly ( I , Q , NS )-fuzzy rough sets) is proposed using fuzzy implications, general quasi overlap functions and fuzzy neighborhood systems, which contains almost all existing fuzzy rough sets. Then, a novel feature selection algorithm (called IQNS-FS algorithm) is proposed and implemented using ( I , Q , NS )-fuzzy rough sets, dependency and specificity measure. The results of 12 datasets indicate that IQNS-FS algorithm performs better than others. Finally, we input the results of IQNS-FS algorithm into single hidden layer neural networks and other classification algorithms, the results illustrate that the IQNS-FS algorithm can be better connected with neural networks than other classification algorithms. The high classification accuracy of single hidden layer neural networks (a very simple structure) further shows that the attributes selected by the IQNS-FS algorithm are important which can express the features of the datasets.

查看原文本刊更多论文

一般准重叠函数和基于模糊邻域系统的模糊粗糙集及其应用

模糊粗糙集是利用现有知识处理数据的重要数学工具。模糊粗糙集已被广泛研究并应用于各个领域，如数据还原和图像处理等。在我们研究的大量文献中，一般准重叠函数和模糊邻域系统分别比现有模糊粗糙集中使用的其他所有模糊算子和知识更广泛。本文利用模糊含义、一般准重叠函数和模糊邻域系统提出了一种新的模糊粗糙集模型（简称（I, Q, NS）-模糊粗糙集），它几乎包含了现有的所有模糊粗糙集。然后，利用（I、Q、NS）-模糊粗糙集、依赖性和特异性度量，提出并实现了一种新的特征选择算法（称为 IQNS-FS 算法）。12 个数据集的结果表明，IQNS-FS 算法的性能优于其他算法。最后，我们将 IQNS-FS 算法的结果输入单隐层神经网络和其他分类算法，结果表明 IQNS-FS 算法与神经网络的连接比其他分类算法更好。单隐层神经网络（一种非常简单的结构）的高分类准确率进一步表明，IQNS-FS 算法选择的属性非常重要，能够表达数据集的特征。

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

IEEE Transactions on Knowledge and Data Engineering 工程技术-工程：电子与电气

CiteScore

11.70

自引率

3.40%

发文量

515

审稿时长

6 months

期刊介绍： The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.