基于多目标的三支决策边界域求解与分类方法OA北大核心CSTPCD

The three-way decision divides the uncertain samples into the boundary domain for delayed decision,but it needs to determine the threshold based on the loss function to divide the boundary domain.However,the loss function usually needs prior knowledge and has a certain degree of subjective,so the ability to divide the boundary domain is insuf-ficient.First,Bayes'rule is used to obtain the conditional probability of the sample;then,three primary goals are estab-lished,including minimizing the uncertainty of the boundary domain,constraining the size of the boundary domain,and reducing the misclassification rate of the whole decision domain.It finds the optimal threshold by incorporating the entropy weight method into TOPSIS(technique for order preference by similarity to an ideal solution)method.This method uses the entropy weight method to calculate the weights of these three goals,obtain the optimal threshold,obtain the boundary domain,and make delayed decisions;finally,a combination of different classifiers is employed to classify the boundary domain.Comparative experiments are carried out on UCI data sets.According to the classification accuracy and F1 value,it shows that the threshold learned by this method can reasonably divide the boundary domain,and the established model can achieve better classification performance.