Please use this identifier to cite or link to this item: https://doi.org/10.1109/MIS.2013.27
Title: Feature ensemble plus sample selection: Domain adaptation for sentiment classification
Authors: Xia, R.
Zong, C.
Hu, X.
Cambria, E. 
Keywords: Domain adaptation
Instance adaptation
Intelligent systems
Labeling adaptation
Sample selection
Sentiment classification
Issue Date: 2013
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
URI: http://scholarbank.nus.edu.sg/handle/10635/116346
ISSN: 15411672
DOI: 10.1109/MIS.2013.27
Appears in Collections:Staff Publications

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