Robust Domain Adaptation Using Active Learning

Traditional machine learning algorithms assume training and test datasets are generated from the same underlying distribution, which is not true for most real-world datasets. As a result, a model trained on the training dataset fails to produce good classification accuracy on the test dataset. One way to mitigate this problem is to use domain adaptation techniques; these techniques build a new model on the unlabeled test dataset (target dataset) by transferring information from a related but labeled training dataset, (source dataset) even when their underlying distributions are different. One other important issue is that in domain adaptation, there is no allowance for obtaining class labels on the test dataset during the training phase. This issue can be handled by active learning techniques that assume the existence of a budget that can be used to label instances on the target domain. Active learning finds the most informative instances of the test dataset that can be labeled by the expert to get a better classification accuracy on the unlabeled test dataset. The goal of this research is to build an optimal classifier on the target dataset by using information related to model complexity. We propose a novel domain adaptation technique using active learning to find the optimal value of a parameter of a class of models that yields the best classifier on the target dataset without assuming the equivalence of the class-conditional probabilities across the domains, unlike other domain adaptation methods. This research also proposes a novel data-alignment technique that allows the use of the source model directly on the target if the distributions differ due to a linear shift, thus avoiding building a complete new classifier on the target domain. Empirical results show that our methods yield better classification accuracy than the state-of-art methods.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Houston

Year dc:date.issued

2016

Author dc:creator

• Dhar Gupta, Kinjal 1982-

Advisor dc:contributor.advisor

• Vilalta, Ricardo

Committee members dc:contributor.committeemember

• Eick, Christoph F.
• Chen, Guoning
• Mahabal, Ashish

• Domain adaptation
• Active learning
• Machine learning
• Model Complexity

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10657/3520

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/3520