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Abstract

Domain adaptation is an important branch of transfer learning. Previous studies have always taken efforts to minimize the optimization goal, but they neglect the relative quality of features or instances. For example, a classic work treats different instances equally in a degree and chooses these instances which minimize the optimization function value. This method will discard these instances that make the data distribution in source and target data domain different and will neglect the instances’ relative quality. To reduce interference between instances in the process of domain adaptation, we put forward a novel method of ODA that uses the overlapping degree to measure every feature or instance’s relative quality and implement feature or instance reweighting. At the same time, we have noticed that there are many parameters with values that will influence the effect of the method. Previous studies do not have a reasonable method to determine the parameters’ values. We can use the genetic algorithm to find the balance between marginal distribution adaptation and conditional distribution adaptation to find the best combination of multiple parameters. Experiments we have done verify that the ODA method outperforms by 3.26% compared with the best comparison method. We have found that our method of finding the optimal parameters can yield more accurate results than the original method.

Keywords

transfer learning data mining domain adaptation instance reweighting

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Biased transfer matching for less overlapping degree for unsupervised domain adaptation

Abstract

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