Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

作     者：Mohsen Eid Tamer Medhat Manal E.Ali 

作者机构：Department of MathematicsFaculty of ScienceTanta UniversityTantaEgypt Department of Electrical EngineeringFaculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Physics and Engineering MathematicsFaculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第45卷第5期

页      面：1531-1544页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Rough set reduction strength of rules similarity matrix 

摘      要：An information system is a type of knowledge representation,and attribute reduction is crucial in big data,machine learning,data mining,and intelligent *** are several ways for solving attribute reduction problems,but they all require a common *** selection of features in most scientific studies is a challenge for the *** working with huge datasets,selecting all available attributes is not an option because it frequently complicates the study and decreases *** the other side,neglecting some attributes might jeopardize data *** this case,rough set theory provides a useful approach for identifying superfluous attributes that may be ignored without sacrificing any significant information;nonetheless,investigating all available combinations of attributes will result in some ***,because attribute reduction is primarily a mathematical issue,technical progress in reduction is dependent on the advancement of mathematical *** the focus of this study is on the mathematical side of attribute reduction,we propose some methods to make a reduction for information systems according to classical rough set theory,the strength of rules and similarity matrix,we applied our proposed methods to several examples and calculate the reduction for each *** methods expand the options of attribute reductions for researchers.