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Query by diverse committee in transfer active learning

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Abstract

Transfer active learning, which is an emerging learning paradigm, aims to actively select informative instances with the aid of transferred knowledge from related tasks. Recently, several studies have addressed this problem. However, how to handle the distributional differences between the source and target domains remains an open problem. In this paper, a novel transfer active learning algorithm is proposed, inspired by the classical query by committee algorithm. Diverse committee members from both domains are maintained to improve the classification accuracy and a mechanism is included to evaluate each member during the iterations. Extensive experiments on both synthetic and real datasets show that our algorithm performs better and is also more robust than the state-of-the-art methods.

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References

  1. Settles B. Active learning literature survey. Technical Report No. 1648, 2010

    Google Scholar 

  2. Rosenstein M T, Z. Marx L P K, Dietterich T G. To transfer or not to transfer. In: Proceedings of NIPS Workshop on Transfer Learning, 2005

    Google Scholar 

  3. Houlsby N, Lobato J M H, Ghahramani Z. Cold-start active learning with robust ordinal matrix factorization. In: Proceedings of the 31st International Conference of Machine Learning. 2014, 766–774

    Google Scholar 

  4. Shao H, Tong B, Suzuki E. Query by committee in a heterogeneous environment. In: Proceedings of the 8th International Conference on Advanced Data Mining and Applications. 2012, 186–198

    Chapter  Google Scholar 

  5. Kale D, Liu Y. Accelerating active learning with transfer learning. In: Proceedings of the 13th IEEE International Conference on Data Mining. 2013, 1085–1090

    Google Scholar 

  6. Chattopadhyay R, Fan W, Davidson I, Panchanathan S, Ye J. Joint transfer and batch-mode active learning. In: Proceedings of the 30th International Conference on Machine Learning. 2013, 253–261

    Google Scholar 

  7. Zhu Z, Zhu X, Ye Y, Guo Y F, Xue X. Transfer active learning. In: Proceedings of the 20th International Conference on Information and Knowledge Management. 2011, 2169–2172

    Google Scholar 

  8. Rai P, Saha A, Daumé III H, Venkatasubramanian S. Domain adaptation meets active learning. In: Proceedings of the NAACL HLT Workshop on Active Learning for Natural Language Processing. 2010, 27–32

    Google Scholar 

  9. Fang M, Yin J, Zhu X. Knowledge transfer for multi-labeler active learning. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2013, 273–288

    Google Scholar 

  10. Li H, Shi Y, Chen M, Hauptmann A G, Xiong Z. Hybrid active learning for cross-domain video concept detection. In: Proceedings of the 18th ACM International Conference on Multimedia. 2010, 1003–1006

    Google Scholar 

  11. Shi X, FanW, Ren J. Actively transfer domain knowledge. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2008, 342–357

    Chapter  Google Scholar 

  12. Luo C, Ji Y, Dai X, Chen J. Active learning with transfer learning. In: Proceedings of ACL Student Research Workshop. 2012, 13–18

    Google Scholar 

  13. Yang L, Hanneke S, Carbonell J. A theory of transfer learning with applications to active learning. Maching Learning, 2013, 90(2): 161–189

    Article  MathSciNet  MATH  Google Scholar 

  14. Caruana R. Multitask learning. In: Thrun S, Pratt L, eds. Leaning to Learn. Springer US, 1998, 95–133

    Google Scholar 

  15. Shao H, Suzuki E. Feature-based inductive transfer learning through minimum encoding. In: Proceedings of the SIAM International Conference on Data Mining. 2011, 259–270

    Google Scholar 

  16. Reichart R, Tomanek K, Hahn U, Rappoport A. Multi-task active learning for linguistic annotations. In: Proceedings of Annual Meeting of the Association for Computational Linguistics. 2008, 861–869

    Google Scholar 

  17. Raj S, Ghosh J, Crawford M M. An active learning approach to knowledge transfer for hyperspectral data analysis. In: Proceedings of IEEE International Conference on Geoscience and Remote Sensing Symposium. 2006, 541–544

    Google Scholar 

  18. Roy N, Mccallum A. Toward optimal active learning through sampling estimation of error reduction. In: Proceedings of the 18th International Conference on Machine Learning. 2011, 441–448

    Google Scholar 

  19. Huang S J, Chen S. Transfer learning with active queries from source domain. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016, 1592–1598

    Google Scholar 

  20. Gao N, Huang S J, Chen S. Multi-label active learning by model guided distribution matching. Frontiers of Computer Science, 2016, 10(5): 845–855

    Article  Google Scholar 

  21. Wallace C, Patrick J. Coding decision trees. Journal of Machine Learning, 1993, 11(1): 7–22

    Article  MATH  Google Scholar 

  22. Quinlan J R, Rivest R L. Inferring decision trees using the minimumdescription length principle. Information and Computation, 1989, 80(3): 227–248

    Article  MathSciNet  MATH  Google Scholar 

  23. Shannon C E. A mathematical theory of communication. Bell System Technical Journal, 1948, 27: 379–423

    Article  MathSciNet  MATH  Google Scholar 

  24. Dagan I, Engelson S P. Committee-based sampling for training probabilistic classifiers. In: Proceedings of the 23rd International Conference on Machine Learning. 2006, 150–157

    Google Scholar 

  25. Lewis D D, Gale W A. A sequential algorithm for training text classifiers. In: Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval. 1994, 3–12

    Google Scholar 

  26. Krause A, Guestrin C. Optimal value of information in graphical models. Journal of Artificial Intelligence, 2009, 35: 557–591

    Article  MathSciNet  MATH  Google Scholar 

  27. Zhang Y. Multi-task active learning with output constraints. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. 2010

    Google Scholar 

  28. Seung H S, Opper M, Sompolinsky H. Query by committee. In: Proceedings of the 5th Annud workshop on Computational Learning Theory. 1992, 287–294

    Google Scholar 

  29. McCallum A, Nigam K. Employing em in pool-based active learning for text classification. In: Proceedings of the 15th International Conference of Machine Learning. 1998, 350–358

    Google Scholar 

  30. Balcan M F, Beygelzimer A, Langford J. Agnostic active learning. In: Proceedings of the 23rd International Conference on Machine Learning. 2006, 65–72

    Google Scholar 

  31. Chang C C, Lin C J. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2001, 2(3): 1–27

    Article  Google Scholar 

  32. Dai W, Yang Q, Xue G R, Yu Y. Boosting for transfer learning. In: Proceedings of the 24th International Conference of Machine Learning. 2007, 193–200

    Google Scholar 

  33. Shi Y, Lan Z, Liu W, Bi W. Extending semi-supervised learning methods for inductive transfer learning. In: Proceedings of IEEE International Conference on Data Mining. 2009, 483–492

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Humanity and Social Science Youth Foundation of the Ministry of Education of China (13YJC630126), SRF for ROCS, SEM, SC-GTEG, the National Natural Science Foundations of China (NSFC) (Grant Nos. 61603240, 71171184, 71201059, and 71201151), the Funds for Creative Research Group of China (70821001), and the Major Program of NSFC (71090401 and 71090400).

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Correspondence to Hao Shao.

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Hao Shao is currently an associate professor at Shanghai University of International Business and Economics, China. He is also the director of The Data Center, Shanghai Center for Global Trade and Economic Governance. He received his PhD in engineering from Kyushu University, Japan. Before moving to Kyushu University, he had been taking a direct PhD course since 2006 at the University of Science and Technology of China, China. He has served as a PC member of international conferences such as IJCAI 2015 and ICACI 2015. He has authored or co-authored more than 30 refereed publications. His current research fields are mainly related to data mining, artificial intelligence, and transfer learning.

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Shao, H. Query by diverse committee in transfer active learning. Front. Comput. Sci. 13, 280–291 (2019). https://doi.org/10.1007/s11704-017-6117-6

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