Hybrid Method of Semi-supervised Learning and Feature Weighted Learning for Domain Adaptation of Document Classification
Hiroyuki Shinnou, Liying Xiao, Minoru Sasaki, Kanako Komiya · Institutional Repositories DataBase (IRDB) · 2015
In regard to document classification, semisupervised learning using the Naive Bayes method and EM algorithm was a great success, and we refer to this method as NBEM in this paper.Although NBEM is also effective for domain adaption of document classification, there is still room for improvement because NBEM does not employ valuable information for this task, that is the difference between source domain and target domain.Here, according to the similarity between the label distribution of the feature on source domain and the estimated label distribution of the feature on target domain, we set the weight on the features to reconstruct the training data.We use this reconstructed training data to perform document classification by NBEM.As a result of experiment by using a part of 20 Newsgroups, the effect of this method was confirmed.