Learning Target Predictive Function without Target Labels

Chun-Wei Seah, Ivor Wai-Hung Tsang, Yew-Soon Ong, Qi Mao
2012 2012 IEEE 12th International Conference on Data Mining  
In the absence of the labeled samples in a domain referred to as target domain, Domain Adaptation (DA) techniques come in handy. Generally, DA techniques assume there are available source domains that share similar predictive function with the target domain. Two core challenges of DA typically arise, variance that exists between source and target domains, and the inherent source hypothesis bias. In this paper, we first propose a Stability Transfer criterion for selecting relevant source domains
more » ... vant source domains with low variance. With this criterion, we introduce a TARget learning Assisted by Source Classifier Adaptation (TARASCA) method to address the two core challenges that have impeded the performances of DA techniques. To verify the robustness of TARASCA, extensive experimental studies are carried out with comparison to several state-of-the-art DA methods on the real-world Sentiment and Newsgroups datasets, where various settings for the class ratios of the source and target domains are considered.
doi:10.1109/icdm.2012.77 dblp:conf/icdm/SeahTOM12 fatcat:zz3r5aj6qrcc5mee3ltqhuhuly