Abstract
When labeled examples are not readily available, active learning and transfer learning are separate efforts to obtain labeled examples for inductive learning. Active learning asks domain experts to label a small set of examples, but there is a cost incurred for each answer. While transfer learning could borrow labeled examples from a different domain without incurring any labeling cost, there is no guarantee that the transferred examples will actually help improve the learning accuracy. To solve both problems, we propose a framework to actively transfer the knowledge across domains, and the key intuition is to use the knowledge transferred from other domain as often as possible to help learn the current domain, and query experts only when necessary. To do so, labeled examples from the other domain (out-of-domain) are examined on the basis of their likelihood to correctly label the examples of the current domain (in-domain). When this likelihood is low, these out-of-domain examples will not be used to label the in-domain example, but domain experts are consulted to provide class label. We derive a sampling error bound and a querying bound to demonstrate that the proposed method can effectively mitigate risk of domain difference by transferring domain knowledge only when they are useful, and query domain experts only when necessary. Experimental studies have employed synthetic datasets and two types of real world datasets, including remote sensing and text classification problems. The proposed method is compared with previously proposed transfer learning and active learning methods. Across all comparisons, the proposed approach can evidently outperform the transfer learning model in classification accuracy given different out-of-domain datasets. For example, upon the remote sensing dataset, the proposed approach achieves an accuracy around 94.5%, while the comparable transfer learning model drops to less than 89% in most cases. The software and datasets are available from the authors.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: Proc. of NIPS 2006 (2007)
Bickel, S., Brückner, M., Scheffer, T.: Discriminative learning for differing training and test distributions. In: Proc. of ICML 2007 (2007)
Daumé III, H., Marcu, D.: Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research 26, 101–126 (2006)
Dai, W., Yang, Q., Xue, G., Yu, Y.: Boosting for transfer learning. In: Proc. of ICML 2007 (2007)
Fan, W., Davidson, I.: On Sample Selection Bias and Its Efficient Correction via Model Averaging and Unlabeled Examples. In: Proc. of SDM 2007 (2007)
Freund, Y., Seung, H., Shamir, E., Tishby, N.: Selective sampling using the Query By Committee algorithm. Machine Learning Journal 28, 133–168 (1997)
Huang, J., Smola, A.J., Gretton, A., Borgwardt, K.M., Schölkopf, B.: Correcting sample selection bias by unlabeled data. In: Proc. of NIPS 2006 (2007)
Körner, C., Wrobel, S.: Multi-class Ensemble-Based Active Learning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212. Springer, Heidelberg (2006)
Lewis, D., Gale, W.: A sequential algorithm for training text classifiers. In: Proc. of SIGIR 1994 (1994)
Ren, J., Shi, X., Fan, W., Yu, P.: Type-Independent Correction of Sample Selection Bias via Structural Discovery and Re-balancing. In: Proc. of SDM 2008 (2008)
Roy, N., McCallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: Proc. of ICML 2001 (2001)
Satpal, S., Sarawagi, S.: Domain adaptation of conditional probability models via feature subsetting. In: Kok, J.N., Koronacki, J., López de Mántaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702. Springer, Heidelberg (2007)
Senung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Proc. 5th Annual ACM Workshop on Computational Learning Theory (1992)
Shimodaira, H.: Inproving predictive inference under covariate shift by weighting the log-likehood function. Journal of Statistical Planning and Inference 90, 227–244 (2000)
Sugiyama, M., Rubens, N.: Active Learning with Model Selection in Linear Regression. In: Proc. of SDM 2008 (2008)
Xing, D., Dai, W., Xue, G., Yu, Y.: Bridged refinement for transfer learning. In: Kok, J.N., Koronacki, J., López de Mántaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702. Springer, Heidelberg (2007)
Xue, Y., Liao, X., Carin, L., Krishnapuram, B.: Multi-task learning for classification with dirichlet process priors. Journal of Machine Learning Research 8, 35–63 (2007)
Xu, Z., Tresp, Y.K., Xu, V., Wang, X.: Representative sampling for text classification using support vector machines. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shi, X., Fan, W., Ren, J. (2008). Actively Transfer Domain Knowledge. In: Daelemans, W., Goethals, B., Morik, K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science(), vol 5212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87481-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-87481-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87480-5
Online ISBN: 978-3-540-87481-2
eBook Packages: Computer ScienceComputer Science (R0)