南京大学学报（自然科学版）2017，Vol.53Issue(6)：1023-1032,10.DOI:10.13232/j.cnki.jnju.2017.06.004
基于可变最小贝叶斯风险的层次多标签分类方法
A method of hierarchical multilabel classification based on variable minimum Bayes risk
摘要
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.关键词
层次分类/多标签分类/可变贝叶斯风险/贝叶斯决策理论Key words
hierarchical classification/multilabel classification/variable Bayes risk/Bayes decision theory分类
信息技术与安全科学引用本文复制引用
徐智康,李旸,李德玉..基于可变最小贝叶斯风险的层次多标签分类方法[J].南京大学学报（自然科学版）,2017,53(6):1023-1032,10.基金项目
国家自然科学基金(61632011,61272095,61432011,U1435212,61573231,61672331) （61632011,61272095,61432011,U1435212,61573231,61672331）
