A Novel Incremental Attribute Reduction Algorithm Based on Intuitionistic Fuzzy Partition Distance

作     者：Pham Viet Anh Nguyen Ngoc Thuy Nguyen Long Giang Pham Dinh Khanh Nguyen The Thuy 

作者机构：Graduate University of Science and TechnologyVietnam Academy of Science and TechnologyHanoi100000Vietnam Institute of Information TechnologyVietnam Academy of Science and TechnologyHanoi100000Vietnam HaUI Institute of TechnologyHanoi University of IndustryHanoi100000Vietnam Faculty of Information TechnologyUniversity of SciencesHue UniversityHue530000Vietnam AI Research DepartmentNeurond Technology JSCHanoi100000Vietnam Information and Communication Technology CenterDepartment of Information and CommunicationsBacninh790000Vietnam 

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47卷第12期

页      面：2971-2988页

学科分类：07[理学] 0701[理学-数学] 

基　　金：funded by Hanoi University of Industry under Grant Number 27-2022-RD/HD-DHCN (URL:https://www.haui.edu.vn/). 

主　　题：Incremental attribute reduction intuitionistic fuzzy sets partition distance measure dynamic decision tables 

摘      要：Attribute reduction,also known as feature selection,for decision information systems is one of the most pivotal issues in machine learning and data mining.Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problemof attribute reduction.Unfortunately,the intuitionistic fuzzy sets based methods have not received much interest,while these methods are well-known as a very powerful approach to noisy decision tables,i.e.,data tables with the low initial classification accuracy.Therefore,this paper provides a novel incremental attribute reductionmethod to dealmore effectivelywith noisy decision tables,especially for highdimensional ones.In particular,we define a new reduct and then design an original attribute reduction method based on the distance measure between two intuitionistic fuzzy partitions.It should be noted that the intuitionistic fuzzypartitiondistance iswell-knownas aneffectivemeasure todetermine important attributes.More interestingly,an incremental formula is also developed to quickly compute the intuitionistic fuzzy partition distance in case when the decision table increases in the number of objects.This formula is then applied to construct an incremental attribute reduction algorithm for handling such dynamic tables.Besides,some experiments are conducted on real datasets to show that our method is far superior to the fuzzy rough set based methods in terms of the size of reduct and the classification accuracy.