ML-KNN: A lazy learning approach to multi-label learning

Pattern Recognition

Volumn 40, Issue 7, 2007, Pages 2038-2048

  Zhang, Min Ling ^a   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

Functional genomics; K nearest neighbor; Lazy learning; Machine learning; Multi label learning; Natural scene classification; Text categorization

Indexed keywords

CLASSIFICATION (OF INFORMATION); GENES; INFORMATION ANALYSIS; LEARNING ALGORITHMS; WEBSITES;

FUNCTIONAL GENOMICS; K-NEAREST NEIGHBOR; LAZY LEARNING; MULTI-LABEL LEARNING; NATURAL SCENE CLASSIFICATION; TEXT CATEGORIZATION;

LEARNING SYSTEMS;

EID: 33947681316     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2006.12.019     Document Type: Article

References (23)

1. A. McCallum, Multi-label text classification with a mixture model trained by EM, in: Working Notes of the AAAI'99 Workshop on Text Learning, Orlando, FL, 1999.
2. Schapire R.E., and Singer Y. Boostexter: a boosting-based system for text categorization. Mach. Learn. 39 2/3 (2000) 135-168
3. Elisseeff A., and Weston J. A kernel method for multi-labelled classification. In: Dietterich T.G., Becker S., and Ghahramani Z. (Eds). Advances in Neural Information Processing Systems 14 (2002), MIT Press, Cambridge, MA 681-687
4. Boutell M.R., Luo J., Shen X., and Brown C.M. Learning multi-label scene classification. Pattern Recognition 37 9 (2004) 1757-1771
5. A. Clare, R.D. King, Knowledge discovery in multi-label phenotype data, in: L.D. Raedt, A. Siebes (Eds.), Lecture Notes in Computer Science, vol. 2168, Springer, Berlin, 2001, pp. 42-53.
6. F.D. Comité, R. Gilleron, M. Tommasi, Learning multi-label alternating decision tree from texts and data, in: P. Perner, A. Rosenfeld (Eds.), Lecture Notes in Computer Science, vol. 2734, Springer, Berlin, 2003, pp. 35-49.
7. D.W. Aha, Special AI review issue on lazy learning, Artif. Intell. Review 11.
8. N. Ueda, K. Saito, Parametric mixture models for multi-label text, in: S. Becker, S. Thrun, K. Obermayer (Eds.), Advances in Neural Information Processing Systems, vol. 15, MIT Press, Cambridge, MA, 2003, pp. 721-728.
9. Sebastiani F. Machine learning in automated text categorization. ACM Compu. Surv. 34 1 (2002) 1-47
10. Salton G. Developments in automatic text retrieval. Science 253 (1991) 974-980
11. Gao S., Wu W., Lee C.-H., and Chua T.-S. A MFoM learning approach to robust multiclass multi-label text categorization. Proceedings of the 21st International Conference on Machine Learning (2004), Banff, Canada 329-336
12. H. Kazawa, T. Izumitani, H. Taira, E. Maeda, Maximal margin labeling for multi-topic text categorization, in: L.K. Saul, Y. Weiss, L. Bottou (Eds.), Advances in Neural Information Processing Systems, vol. 17, MIT Press, Cambridge, MA, 2005, pp. 649-656.
13. Freund Y., and Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55 1 (1997) 119-139
14. Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. B 39 1 (1977) 1-38
15. Freund Y., and Mason L. The alternating decision tree learning algorithm. Proceedings of the 16th International Conference on Machine Learning (1999), Bled, Slovenia 124-133
16. R.E. Schapire, Y. Singer, Improved boosting algorithms using confidence-rated predictions, in: Proceedings of the 11th Annual Conference on Computational Learning Theory, New York, 1998, pp. 80-91.
17. Gao S., Wu W., Lee C.-H., and Chua T.-S. A maximal figure-of-merit learning approach to text categorization. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2003), Toronto, Canada 174-181
18. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufmann, San Mateo, California
19. R. Jin, Z. Ghahramani, Learning with multiple labels, in: S. Becker, S. Thrun, K. Obermayer (Eds.), Advances in Neural Information Processing Systems, vol. 15, MIT Press, Cambridge, MA, 2003, pp. 897-904.
20. Pavlidis P., Weston J., Cai J., and Grundy W.N. Combining microarray expression data and phylogenetic profiles to learn functional categories using support vector machines. Proceedings of the Fifth Annual International Conference on Computational Biology (2001), Montréal, Canada 242-248
21. Yang Y., and Pedersen J.O. A comparative study on feature selection in text categorization. Proceedings of the 14th International Conference on Machine Learning (1997), Nashville, TN 412-420
22. Dumais S.T., Platt J., Heckerman D., and Sahami M. Inductive learning algorithms and representation for text categorization. Proceedings of the Seventh ACM International Conference on Information and Knowledge Management (1998), Bethesda, MD 148-155
23. Zhang M.-L., and Zhou Z.-H. Multilabel neural networks with applications to functional genomics and text categorization. IEEE T. Knowl. Data En. 18 10 (2006) 1338-1351