An Iterative Similarity based Adaptation Technique for Cross-domain Text Classification
Himanshu S. Bhatt, Deepali Semwal, Shourya Roy · 2015
Supervised machine learning classification algorithms assume both train and test data are sampled from the same domain or distribution.However, performance of the algorithms degrade for test data from different domain.Such cross domain classification is arduous as features in the test domain may be different and absence of labeled data could further exacerbate the problem.This paper proposes an algorithm to adapt classification model by iteratively learning domain specific features from the unlabeled test data.Moreover, this adaptation transpires in a similarity aware manner by integrating similarity between domains in the adaptation setting.Cross-domain classification experiments on different datasets, including a real world dataset, demonstrate efficacy of the proposed algorithm over state-of-theart.