摘要
为了解决目前所提出的多标签分类算法仍然存在分类精度低和计算复杂度高的问题,提出了一种基于质心的多标签引力模型(ML-GM)。在训练阶段,通过计算文档与类的质心之间的相似性来获得相似性区间。在测试阶段,通过比较未定义文档和类质心之间的相似性是否在相似性区间内来进行多标签分类。该模型通过引入质心分类器和引力模型(GM)解决了计算复杂度高、分类精度低的问题。在实验中使用了雅虎数据集,结果表明,ML-GM在平均精确度、AUC、1-错误率和汉明损失上都有优越性。
In order to solve the problem that the current multi-label classification algorithm has low classification accuracy and high computational complexity,a centroid-based multi-label model for text categorization,named Multi-label Gravitation Model(ML-GM),is proposed.In the training phase,a similarity interval by calculating the similarity between the document and the centroid of the class.In the test phase,multi-label classification is performed by comparing the similarity between the undefined document and the class centroid is within the similarity interval.The model solves the problem of high computational complexity and low classification accuracy by introducing a centroid classifier and a gravity model.The Yahoo dataset is used in the experiment,and the results show that ML-GM achieves supe-rior performance in terms of average accuracy,AUC,one-error and hamming loss.
作者
李校林
王成
LI Xiao-lin;WANG Cheng(College of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065;Research Center of New Telecommunication Technology Applications,Chongqing University of Posts and Telecommunications,Chongqing 400065;Chongqing Information Technology Designing Limited Company,Chongqing 400021,China)
出处
《计算机工程与科学》
CSCD
北大核心
2020年第6期1120-1126,共7页
Computer Engineering & Science
关键词
文本分类
质心分类器
多标签学习
引力模型
相似度区间
text classification
centroid-based classifier
multi-label learning
gravitation model
similarity interval
