Abstract:  Hierarchical multilabel classification(HMC) method organizes labels into a hierarchical structure based on the correlation among the labels which can be as a kind of supervised information，so that to better solve the multilabel classification problem.There are two commonly used methods in hierarchical multilabel classification problem.One can be called loss independent method，which does not use any loss function in training model and prediction process.The other is called loss sensitive method.For loss sensitive method，a frequently－used loss function in HMC is HMC－loss，which assigns two kinds of errors of false positive and false negative with different weights.At the same time，the hierarchical information is added to the loss function according to the location in the hierarchy.In the prediction process by using HMC－loss，although the loss value is ideal，the number of predicted positive labels are far more than the actual label number.In addition，introducing hierarchy information into HMC－loss may have a negative effect to the decision order of label nodes.To solve these problems，we firstly propose an improved loss function IMH－loss(Improved Hierarchical loss) which deletes the hierarchical information so that the decision order of the nodes is guaranteed.By using Bayesian decision theory，we then propose a hierarchical multilabel classification method which can change Bayes risk along with the decision process.The experimental results on some real－world data sets show that the presented method can improve the predicted accuracy of labels while ensuring the recall rate and the prediction results is closer to the real results.