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Instance-based Domain Adaptation via Multiclustering Logistic Approximation
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DOI：10.1109/MIS.2018.012001555.png)
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Abstract
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With the explosive growth in the number of online texts, we could easily collect a large amount of labeled training data from different source domains. However, a basic assumption in building statistical machine learning models for sentiment analysis is that the training and test data must be drawn from the same distribution. Otherwise, directly training a statistical model usually results in poor performance. Faced with the massive amount of labeled data from different domains, it is important to identify the source-domain training instances that are closely relevant to the target domain and make better use of them. In this work, we propose a new approach, called multiclustering logistic approximation (MLA), to address this problem. In MLA, we adapt the source-domain training data to the target domain via a framework of multiclustering logistic approximation. Experimental results demonstrate that MLA has significant advantages over the state-of-the-art instance adaptation methods, especially in the scenario of multidistributional training data.
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