山西大学学报（自然科学版）2024，Vol.47Issue(1)：40-47,8.DOI:10.13451/j.sxu.ns.2023133
基于邻域粒化的逻辑回归算法
Logistic Regression Algorithm Based on Neighborhood Granulation
摘要
Abstract
As a classical classification algorithm,logistic regression has a simple structure and strong interpretability.However,logis-tic regression is difficult to deal with fuzzy and uncertain nonlinear data.To solve this problem,a logistic regression algorithm based on neighborhood granulation is proposed by using neighborhood granulation technology in granular computing theory.For nonlinear data,neighborhood granulation makes the data easier to separate and construct.Firstly,the neighborhood granules are constructed by the neighborhood granulation of the single feature of the data set sample.The neighborhood granular vectors are then formed on the multi-feature.In addition,the measurement and operation rules of these neighborhood granular vectors are defined,and a neighbor-hood granular logistic regression algorithm is designed,which effectively improves the classification accuracy of logistic regression.Classification experiments are carried out on WDBC(Diagnostic Wisconsin Breast Cancer),Iris and Seeds data sets,and compared with the classical logistic regression.The results show that the classification accuracy of the proposed algorithm is 0.6%,7.6%and 4.1%higher than that of the classical logistic regression in the three data sets,respectively.关键词
逻辑回归/单特征粒化/粒计算/邻域粒子/粒向量Key words
logistic regression/single feature granulation/granular computing/neighborhood granules/granular vectors分类
信息技术与安全科学引用本文复制引用
闫静茹,陈颖悦,曾高发,刘培谦,傅兴宇..基于邻域粒化的逻辑回归算法[J].山西大学学报（自然科学版）,2024,47(1):40-47,8.基金项目
厦门市科技计划项目(2022CXY0428) （2022CXY0428）
