A survey of transfer learning <s> Hybrid-based (instance and parameter) transfer learning <s> In this paper, we focus on the tasks of cross-domain sentiment classification. We find across different domains, features with some types of part-of-speech (POS) tags are domain-dependent, while some others are domain-free. Based on this finding, we proposed a POS-based ensemble model to efficiently integrate features with different types of POS tags to improve the classification performance. Weights are trained by stochastic gradient descent (SGD) to optimize the perceptron and minimal classification error (MCE) criteria. Experimental results show that the proposed ensemble model is quite effective for the task of cross-domain sentiment classification. <s> BIB001 </s> A survey of transfer learning <s> Hybrid-based (instance and parameter) transfer learning <s> 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. <s> BIB002
The paper by Xia BIB002 proposes a two step approach to address marginal distribution differences and conditional distribution differences between the source and target domains called the sample selection and feature ensemble (SSFE) method. A sample selection process, using a modified version of principal component analysis, is employed to select labeled source domain samples such that the source and target marginal distributions are equalized. Next, a feature ensemble step attempts to resolve the conditional distribution differences between the source and target domains. Four individual classifiers are defined corresponding to parts of speech of noun, verb, adverb/adjective, and other. The four classifiers are trained using only the features that correspond to that part of speech. The training data is the limited labeled target and the labeled source selected in the previous sample selection step. The four classifiers are weighted as a function of minimizing the classification error using the limited labeled target data. The weighted output of the four classifiers is used as the final target classifier. This work by Xia BIB002 extends the earlier work of Xia BIB001 . The experiments are performed for the application of review sentiment classification using four different review categories, where each category is combined to create 12 different source and target pairs. Classification accuracy is measured as the performance metric. A baseline approach using all the training data from the source is constructed, along with a sample selection approach (only using the first step defined above), a feature ensemble approach (only using the second step defined above) and the complete approach outlined above. The complete approach is the best performing, followed by sample selection and feature ensemble approaches, and the baseline approach. The sample selection and feature ensemble approaches perform equally as well in head-to-head tests. The weighting of the four classifiers (defined by the corresponding parts of speech) in the procedure above gives limited resolution in attempting to adjust for context feature bias issues. A method of having more classifiers in the ensemble step could yield better performance at the expense of higher complexity.