Domain adaptation for classification problem

Domain Adaptation (DA) is a learning problem where we wish to leverage labeled data from source domain to learn a hypothesis performing well on a different but related target domain for which no labeled data is available. In this paper, an idea of unified method named UNDA (United Domain Adaptation) is used for DA problems. The main contents are: (1) Find a feature mapping function to bridge the gap between two domains, where feature representation is learned from the source domain for the target domain. (2) Find a discriminant function that is adapted from the source domain instances to the target domain instances step to step, where iteratively deleting source domain instances and adding target domain instances. Experimental results on two data sets show that our new approaches can be successfully used to perform domain adaptation for text and sentiment classification problems. © 2013 Binary Information Press.