基于邻域粒化的逻辑回归算法OACSTPCD

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.