Please use this identifier to cite or link to this item:
|Title:||Feature ensemble plus sample selection: Domain adaptation for sentiment classification|
|Citation:||Xia, R., Zong, C., Hu, X., Cambria, E. (2013). Feature ensemble plus sample selection: Domain adaptation for sentiment classification. IEEE Intelligent Systems 28 (3) : 10-18. ScholarBank@NUS Repository. https://doi.org/10.1109/MIS.2013.27|
|Abstract:||Domain adaptation problems often arise often in the field of sentiment classification. Here, the feature ensemble plus sample selection (SS-FE) approach is proposed, which takes labeling and instance adaptation into account. A feature ensemble (FE) model is first proposed to learn a new labeling function in a feature reweighting manner. Furthermore, a PCA-based sample selection (PCA-SS) method is proposed as an aid to FE. Experimental results show that the proposed SS-FE approach could gain significant improvements, compared to FE or PCA-SS, because of its comprehensive consideration of both labeling adaptation and instance adaptation. © 2013 IEEE.|
|Source Title:||IEEE Intelligent Systems|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 21, 2019
WEB OF SCIENCETM
checked on Feb 12, 2019
checked on Feb 8, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.