A transductive multi-label classification method for weak labeling

Jisuanji Yanjiu yu Fazhan/Computer Research and Development

Volumn 47, Issue 8, 2010, Pages 1392-1399

  Kong, Xiangnan ^a   Li, Ming ^a   Jiang, Yuan ^a   Zhou, Zhihua ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

Machine learning; Multi label classification; Scene classification; Transductive learning; Weak labeling

Indexed keywords

MACHINE-LEARNING; MULTI-LABEL CLASSIFICATIONS; SCENE CLASSIFICATION; TRANSDUCTIVE LEARNING; WEAK LABELING;

BIOINFORMATICS; COMPUTER AIDED DIAGNOSIS; LEARNING ALGORITHMS; LEARNING SYSTEMS; TEXT PROCESSING;

CLASSIFICATION (OF INFORMATION);

EID: 77956464907     PISSN: 10001239     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. Schapire R E, Singer Y. Boostexter: A boosting-based system for text categorization [J]. Machine Learning, 2000, 39(2/3): 135-168.
2. Elisseeff A, Weston J. A kernel method for multi-labelled classification [C] //Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2002: 681-687.
3. Zhang M -L, Zhou Z -H. Ml-kNN: A lazy learning approach to multi-label learning [J]. Pattern Recognition, 2007, 40(7): 2038-2048.
4. Zhang M -L, Zhou Z -H. Multi-label neural networks with applications to functional genomics and text categorization [J]. IEEE Trans on Knowledge and Data Engineering, 2006, 18(10): 1338-1351.
5. Zhou Zhihua, Zhang Minling, Huang Shengjun, et al. MIML: A Framework for Learning with Ambiguous Objects, 0808.3231 [R]. Nanjing: National Key Laboratory for Novel Software Technology of Nanjing University, 2008 (in Chinese)
6. Comite F D, Gilleron R, Tommasi M. Learning multi-label alternating decision tree from texts and data [C] //Proc of the 3rd Int Conf on Machine Learning and Data Mining in Pattern Recognition. Berlin: Springer, 2003: 35-49.
7. Gao S, Wu W, Lee C -H, et al. A MFoM learning approach to robust multiclass multi-label text categorization [C] //Proc of the 21st Int Conf on Machine Learning. New York: ACM, 2004: 329-336.
8. Kazawa H, Izumitani T, Taira H, et al. Maximal margin labeling for multi-topic text categorization [C] //Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2005: 649-656.
9. McCallum A. Multi-label text classification with a mixture model trained by EM [C] //Working Notes of the AAAI'99 Workshop on Text Learning. Menlo Park, CA: AAAI, 1999: 1-7.
10. Boutell M R, Luo J, Shen X, Brown C M. Learning multi-label scene classification [J]. Pattern Recognition, 2004, 37(9): 1757-1771.
11. Rak R, Kurgan L, Reformat M. Multi-label associative classification of medical documents from MEDLINE [C] //Proc of the 4th Int Conf on Machine Learning and Applications. Washington, DC: IEEE Computer Society, 2005: 177-186.
12. Thabtah F A, Cowling P I, Peng Y. MMAC: A new multi-class, multi-label associative classification approach [C] //Proc of the 4th Int Conf on Data Mining. Washington, DC: IEEE Computer Society, 2004: 217-224.
13. Vapnik V N. Statistical Learning Theory [M]. New York: Wiley, 1998.
14. Jiang Yuan, She Qiaoqiao, Li Ming, et al. A transductive multi-label text categorization approach [J]. Journal of Computer Research and Development, 2008, 45(11): 1817-1823 (in Chinese)
15. Li Ming, Zhou Zhihua. Online semi-supervised learning with multi-kernel ensemble [J]. Journal of Computer Research and Development, 2008, 45(12): 2060-2068 (in Chinese)
16. Zhou D, Bousquet O, Lal T N, et al. Learning with local and global consistency [C] //Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2004: 321-328