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|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|
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