Query by diverse committee in transfer active learning

doi:10.1007/s11704-017-6117-6

Front. Comput. Sci.

2019, Vol. 13

Issue (2) : 280-291 https://doi.org/10.1007/s11704-017-6117-6

RESEARCH ARTICLE

Query by diverse committee in transfer active learning

Hao SHAO(

)

WTO School, Shanghai University of International Business and Economics, Shanghai 200336, China

Download: PDF(620 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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.

Keywords transfer learning active learning machine learning

Corresponding Author(s): Hao SHAO

Just Accepted Date: 21 February 2017 Online First Date: 20 March 2018 Issue Date: 08 April 2019

Cite this article:

Hao SHAO. Query by diverse committee in transfer active learning[J]. Front. Comput. Sci., 2019, 13(2): 280-291.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-017-6117-6
https://academic.hep.com.cn/fcs/EN/Y2019/V13/I2/280

1	BSettles. Active learning literature survey. Technical Report No. 1648, 2010
2	M TRosenstein, L P K Z. Marx, T GDietterich. To transfer or not to transfer. In: Proceedings of NIPS Workshop on Transfer Learning. 2005
3	NHoulsby, J M HLobato, ZGhahramani. Cold-start active learning with robust ordinal matrix factorization. In: Proceedings of the 31st International Conference of Machine Learning. 2014, 766–774
4	HShao, BTong, ESuzuki. Query by committee in a heterogeneous environment. In: Proceedings of the 8th International Conference on Advanced Data Mining and Applications. 2012, 186–198 https://doi.org/10.1007/978-3-642-35527-1_16
5	DKale, YLiu. Accelerating active learning with transfer learning. In: Proceedings of the 13th IEEE International Conference on Data Mining. 2013, 1085–1090 https://doi.org/10.1109/ICDM.2013.160
6	RChattopadhyay, WFan, IDavidson, S Panchanathan, JYe. Joint transfer and batch-mode active learning. In: Proceedings of the 30th International Conference on Machine Learning. 2013, 253–261
7	ZZhu, XZhu, YYe, Y FGuo, XXue. Transfer active learning. In: Proceedings of the 20th International Conference on Information and Knowledge Management. 2011, 2169–2172 https://doi.org/10.1145/2063576.2063918
8	PRai, ASaha, III HDaumé, SVenkatasubramanian. Domain adaptation meets active learning. In: Proceedings of the NAACL HLT Workshop on Active Learning for Natural Language Processing. 2010, 27–32
9	MFang, JYin, XZhu. Knowledge transfer for multi-labeler active learning. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2013, 273–288 https://doi.org/10.1007/978-3-642-40988-2_18
10	HLi, YShi, MChen, A G Hauptmann, ZXiong. Hybrid active learning for cross-domain video concept detection. In: Proceedings of the 18th ACM International Conference on Multimedia. 2010, 1003–1006 https://doi.org/10.1145/1873951.1874135
11	XShi, WFan, JRen. Actively transfer domain knowledge. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2008, 342–357 https://doi.org/10.1007/978-3-540-87481-2_23
12	CLuo, YJi, XDai, J Chen. Active learning with transfer learning. In: Proceedings of ACL Student Research Workshop. 2012, 13–18
13	LYang, S Hanneke, JCarbonell. A theory of transfer learning with applications to active learning. Maching Learning, 2013, 90(2): 161–189 https://doi.org/10.1007/s10994-012-5310-y
14	RCaruana. Multitask learning. In: Thrun S, Pratt L, eds. Leaning to Learn. Springer US, 1998, 95–133 https://doi.org/10.1007/978-1-4615-5529-2_5
15	HShao, ESuzuki. Feature-based inductive transfer learning through minimum encoding. In: Proceedings of the SIAM International Conference on Data Mining. 2011, 259–270 https://doi.org/10.1137/1.9781611972818.23
16	RReichart, K Tomanek, UHahn, ARappoport. Multi-task active learning for linguistic annotations. In: Proceedings of Annual Meeting of the Association for Computational Linguistics. 2008, 861–869
17	SRaj, JGhosh, M MCrawford. An active learning approach to knowledge transfer for hyperspectral data analysis. In: Proceedings of IEEEInternational Conference on Geoscience and Remote Sensing Symposium. 2006, 541–544
18	NRoy, A Mccallum. Toward optimal active learning through sampling estimation of error reduction. In: Proceedings of the 18th International Conference on Machine Learning. 2011, 441–448
19	S JHuang, SChen. Transfer learning with active queries from source domain. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016, 1592–1598
20	NGao, S JHuang, SChen. Multi-label active learning by model guided distribution matching. Frontiers of Computer Science, 2016, 10(5): 845–855 https://doi.org/10.1007/s11704-016-5421-x
21	CWallace, J Patrick. Coding decision trees. Journal of Machine Learning, 1993, 11(1): 7–22 https://doi.org/10.1023/A:1022646101185
22	J RQuinlan, R LRivest. Inferring decision trees using the minimumdescription length principle. Information and Computation, 1989, 80(3): 227–248 https://doi.org/10.1016/0890-5401(89)90010-2
23	C EShannon. A mathematical theory of communication. Bell System Technical Journal, 1948, 27: 379–423 https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
24	IDagan, S P Engelson. Committee-based sampling for training probabilistic classifiers. In: Proceedings of the 23rd International Conference on Machine Learning. 2006, 150–157
25	D DLewis, W AGale. 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 https://doi.org/10.1007/978-1-4471-2099-5_1
26	AKrause, C Guestrin. Optimal value of information in graphical models. Journal of Artificial Intelligence, 2009, 35: 557–591
27	YZhang. Multi-task active learning with output constraints. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. 2010
28	H SSeung, MOpper, HSompolinsky. Query by committee. In: Proceedings of the 5th Annud workshop on Computational Learning Theory. 1992, 287–294
29	AMcCallum, KNigam. Employing em in pool-based active learning for text classification. In: Proceedings of the 15th International Conference of Machine Learning. 1998, 350–358
30	M FBalcan, A Beygelzimer, JLangford. Agnostic active learning. In: Proceedings of the 23rd International Conference on Machine Learning. 2006, 65–72 https://doi.org/10.1145/1143844.1143853
31	C CChang, C JLin. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2001, 2(3): 1–27 https://doi.org/10.1145/1961189.1961199
32	W,Dai QYang , G RXue, Y Yu. Boosting for transfer learning. In: Proceedings of the 24th International Conference of Machine Learning. 2007, 193–200 https://doi.org/10.1145/1273496.1273521
33	YShi, ZLan, WLiu, W Bi. Extending semi-supervised learning methods for inductive transfer learning. In: Proceedings of IEEE International Conference on Data Mining. 2009, 483–492 https://doi.org/10.1109/ICDM.2009.75

[1]	Xia-an BI, Yiming XIE, Hao WU, Luyun XU. Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm[J]. Front. Comput. Sci., 2021, 15(6): 156903-.
[2]	Yan-Ping SUN, Min-Ling ZHANG. Compositional metric learning for multi-label classification[J]. Front. Comput. Sci., 2021, 15(5): 155320-.
[3]	Jian SUN, Pu-Feng DU. Predicting protein subchloroplast locations: the 10th anniversary[J]. Front. Comput. Sci., 2021, 15(2): 152901-.
[4]	Syed Farooq ALI, Muhammad Aamir KHAN, Ahmed Sohail ASLAM. Fingerprint matching, spoof and liveness detection: classification and literature review[J]. Front. Comput. Sci., 2021, 15(1): 151310-.
[5]	Xu-Ying LIU, Sheng-Tao WANG, Min-Ling ZHANG. Transfer synthetic over-sampling for class-imbalance learning with limited minority class data[J]. Front. Comput. Sci., 2019, 13(5): 996-1009.
[6]	Yu-Feng LI, De-Ming LIANG. Safe semi-supervised learning: a brief introduction[J]. Front. Comput. Sci., 2019, 13(4): 669-676.
[7]	Wenhao ZHENG, Hongyu ZHOU, Ming LI, Jianxin WU. CodeAttention: translating source code to comments by exploiting the code constructs[J]. Front. Comput. Sci., 2019, 13(3): 565-578.
[8]	Qingying SUN, Zhongqing WANG, Shoushan LI, Qiaoming ZHU, Guodong ZHOU. Stance detection via sentiment information and neural network model[J]. Front. Comput. Sci., 2019, 13(1): 127-138.
[9]	Ruochen HUANG, Xin WEI, Liang ZHOU, Chaoping LV, Hao MENG, Jiefeng JIN. A survey of data-driven approach on multimedia QoE evaluation[J]. Front. Comput. Sci., 2018, 12(6): 1060-1075.
[10]	Qiang LV, Yixin CHEN, Zhaorong LI, Zhicheng CUI, Ling CHEN, Xing ZHANG, Haihua SHEN. Achieving data-driven actionability by combining learning and planning[J]. Front. Comput. Sci., 2018, 12(5): 939-949.
[11]	Ashish Kumar DWIVEDI, Anand TIRKEY, Santanu Kumar RATH. Software design pattern mining using classification-based techniques[J]. Front. Comput. Sci., 2018, 12(5): 908-922.
[12]	Bo SUN, Haiyan CHEN, Jiandong WANG, Hua XIE. Evolutionary under-sampling based bagging ensemble method for imbalanced data classification[J]. Front. Comput. Sci., 2018, 12(2): 331-350.
[13]	Min-Ling ZHANG, Yu-Kun LI, Xu-Ying LIU, Xin GENG. Binary relevance for multi-label learning: an overview[J]. Front. Comput. Sci., 2018, 12(2): 191-202.
[14]	Zhongqing WANG, Shoushan LI, Guodong ZHOU. Personal summarization from profile networks[J]. Front. Comput. Sci., 2017, 11(6): 1085-1097.
[15]	Qiang LU,Zhicheng CUI,Yixin CHEN,Xiaoping CHEN. Extracting optimal actionable plans from additive tree models[J]. Front. Comput. Sci., 2017, 11(1): 160-173.

Viewed

Full text

Abstract

Cited

Shared

Discussed