OSFS-Vague: Online streaming feature selection algorithm based on vague set  

作　　者：Jie Yang Zhijun Wang Guoyin Wang Yanmin Liu Yi He Di Wu 

机构地区：[1]School of Physics and Electronic Science,Zunyi Normal University,Zunyi,China [2]Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications,Chongqing,China [3]Department of Computer Science,Old Dominion University,Norfolk,Virginia,USA [4]College of Computer and Information Science,Southwest University,Chongqing,China

出　　处：《CAAI Transactions on Intelligence Technology》2024年第6期1451-1466,共16页智能技术学报(英文)

基　　金：Science and Technology Project of Zunyi,Grant/Award Number:ZSKRPT[2023]3;Excellent Young Scientific and Technological Talents Foundation of Guizhou Province,Grant/Award Number:QKHplatform talent(2021)5627;Science and Technology Top Talent Project of Guizhou Education Department,Grant/Award Number:QJJ2022(088);the Guizhou Provincial Department of Education Colleges and Universities Science and Technology Innovation Team,Grant/Award Number:QJJ[2023]084;National Natural Science Foundation of China,Grant/Award Numbers:62066049,62221005,61936001,62176070;Department of Education of Guizhou Province,Grant/Award Number:QJJ[2023]084;Science and Technology Foundation of State Grid Corporation of China,Grant/Award Number:1400-202357341A-1-1-ZN。

摘　　要：Online streaming feature selection(OSFS),as an online learning manner to handle streaming features,is critical in addressing high-dimensional data.In real big data-related applications,the patterns and distributions of streaming features constantly change over time due to dynamic data generation environments.However,existing OSFS methods rely on presented and fixed hyperparameters,which undoubtedly lead to poor selection performance when encountering dynamic features.To make up for the existing shortcomings,the authors propose a novel OSFS algorithm based on vague set,named OSFSVague.Its main idea is to combine uncertainty and three-way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous method.OSFS-Vague also improves the calculation method of correlation between features and labels.Moreover,OSFS-Vague uses the distance correlation coefficient to classify streaming features into relevant features,weakly redundant features,and redundant features.Finally,the relevant features and weakly redundant features are filtered for an optimal feature set.To evaluate the proposed OSFS-Vague,extensive empirical experiments have been conducted on 11 datasets.The results demonstrate that OSFS-Vague outperforms six state-of-the-art OSFS algorithms in terms of selection accuracy and computational efficiency.

关 键 词：feature selection online feature selection three-way decision vague set 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]