基于决策树集成的偏标记学习算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201604009

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Abstract To overcome the problem of the missing supervision information in partial label learning, a special splitting measure for the generation of decision tree is designed according to the property of partial label examples and the growth algorithm of decision tree is modified. In the proposed algorithm, bootstrap sampling is employed to construct multiple decision trees, and then the final prediction result is obtained by voting on the classification results of each decision tree. Experiments on artificial datasets and real-world datasets validate the good performance of the proposed algorithm.

Key words： Weakly Supervised Learning Partial Label Learning Random Forest Ensemble Learning

Received: 15 May 2015

ZTFLH:

TP 301

Fund:Supported by National Natural Science Foundation of China (No.61573104,61222309), MOE Program for New Century Excellent Talents in University (No. NCET-13-0130)

About author:: (YU Fei， born in 1990， master student. His research interests include machine learning and data mining.)
(ZHANG Minling(Corresponding author)， born in 1979， Ph.D.， professor. His research interests include machine learning and data mining.)

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Cite this article:

YU Fei,ZHANG Minling. Decision Tree Ensemble Based Partial Label Learning Algorithm[J]. , 2016, 29(4): 367-375.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201604009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I4/367

[1] JIN R, GHAHRAMANI Z. Learning with Multiple Labels // BECKER S, THRUN S, OBERMAYER K, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2003, 15: 897-904.
[2] COUR T, SAPP B, TASKAR B. Learning from Partial Labels. Journal of Machine Learning Research, 2011, 12: 1501-1536.
[3] RAYKAR V C, YU S P, ZHAO L H, et al. Learning from Crowds. Journal of Machine Learning Research, 2010, 11: 1297-1322.
[4] ZENG Z N, XIAO S J, JIA K, et al. Learning by Associating Ambi-guously Labeled Images // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 708-715.
[5] COUR T, SAPP B, JORDAN C, et al. Learning from Ambiguously Labeled Images // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009: 919-926.
[6] LIU L P, DIETTERICH T G. A Conditional Multinomial Mixture Model for Superset Label Learning // BARTLETT P, PEREIRA F C N, BURGES C J C, et al., eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2012, 25: 557-565.
[7] NGUYEN N, CARUANA R. Classification with Partial Labels // Proc of the 14th ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining. Las Vegas, USA, 2008: 551-559.
[8] HLLERMEIER E, BERINGER J. Learning from Ambiguously Labeled Examples // Proc of the 6th International Conference on Advances in Intelligen Data Analysis. Madrid, Spain, 2005: 168-179.
[9] ZHOU Z H. Ensemble Methods: Foundations and Algorithms. Boca Raton, USA: Chapman & Hall, 2012.
[10] BREIMAN L. Random Forests. Machine Learning, 2001, 45(1): 5-32.
[11] FREUND Y, SCHAPIRE R E. Experiments with a New Boosting Algorithm // Proc of the 13th International Conference on Machine Learning. Bari, Italy, 1996: 148-156.
[12] ZHU X J. Semi-supervised Learning Literature Survey. Computer Sciences TR, 1530. Madison, USA: University of Wisconsin-Ma-dison, 2008.
[13] ZHANG M L, ZHOU Z H. A Review on Multi-label Learning Algorithms. IEEE Trans on Knowledge and Data Engineering, 2014, 26(8): 1819-1837.
[14] AMORES J. Multiple Instance Classification: Review, Taxonomy and Comparative Study. Artificial Intelligence, 2013, 201: 81-105.
[15] ZHOU Z H, ZHANG M L, HUANG S J, et al. Multi-instance Multi-label Learning. Artificial Intelligence, 2012, 176(1): 2291-2320.
[16] GRANDVALET Y. Logistic Regression for Partial Labels // Proc of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. Annecy, France, 2002: 1935-1941.
[17] CHEN Y C, PATEL V M, PILLAI J K, et al. Dictionary Learning from Ambiguously Labeled Data // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 353-360.
[18] ZHANG M L. Disambiguation-Free Partial Label Learning // Proc of the 14th SIAM International Conference on Data Mining. Philadelphia, USA, 2014: 37-45.
[19] BREIMAN L, FREIDMAN J, STONE C J, et al. Classification and Regression Trees. Boca Raton, USA: CRC Press, 1984.
[20] BRIGGS F, FERN X Z, RAICH R. Rank-Loss Support Instance Machines for MIML Instance Annotation // Proc of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing, China, 2012: 534-542.
[21] WINN J, CRIMINISI A, MINKA T. Object Categorization by Learned Universal Visual Dictionary // Proc of the 10th IEEE International Conference on Computer Vision. Beijing, China, 2005: 1800-1807.