TY - JOUR
T1 - 融合样本相似性的弱监督多标签分类
AU - Luo, Senlin
AU - Wang, Haizhou
AU - Pan, Limin
AU - Sun, Xiaoguang
N1 - Publisher Copyright:
© 2021, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
PY - 2021/7
Y1 - 2021/7
N2 - Multilabel classification is a machine learning method to improve the performance of multi label joint decision by label correlation. In practical application scenarios, data labels are easy to be incomplete, which can lead to the reduction of available training data, and it is difficult to train the model adequately. Moreover, it is easy to cause the increase of label distribution variance, the deviation of correlation knowledge, and the limitation of multi label classification effect. To solve the problems, a weak supervised multi label classification method based on sample similarity was proposed. The method was arranged to use label correlation and sample similarity to recover labels to improve data utilization, and to embed label recovery into the training process to correct the bias in the model learning process. Based on the proximal accelerated gradient method, parameter optimization was carried out, and a multi label classification model was established for weak supervised learning scene. Experiments were completed with real data set. The results show that the method can effectively improve the classification ability of the model for the incomplete labels according to the similarity of samples, possessing high practical value.
AB - Multilabel classification is a machine learning method to improve the performance of multi label joint decision by label correlation. In practical application scenarios, data labels are easy to be incomplete, which can lead to the reduction of available training data, and it is difficult to train the model adequately. Moreover, it is easy to cause the increase of label distribution variance, the deviation of correlation knowledge, and the limitation of multi label classification effect. To solve the problems, a weak supervised multi label classification method based on sample similarity was proposed. The method was arranged to use label correlation and sample similarity to recover labels to improve data utilization, and to embed label recovery into the training process to correct the bias in the model learning process. Based on the proximal accelerated gradient method, parameter optimization was carried out, and a multi label classification model was established for weak supervised learning scene. Experiments were completed with real data set. The results show that the method can effectively improve the classification ability of the model for the incomplete labels according to the similarity of samples, possessing high practical value.
KW - Incomplete labels
KW - Multilabel classification
KW - Sample similarity
UR - http://www.scopus.com/inward/record.url?scp=85111474505&partnerID=8YFLogxK
U2 - 10.15918/j.tbit1001-0645.2020.117
DO - 10.15918/j.tbit1001-0645.2020.117
M3 - 文章
AN - SCOPUS:85111474505
SN - 1001-0645
VL - 41
SP - 745
EP - 751
JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
IS - 7
ER -