Statistical topic models for multi-label document classification

Machine Learning

Volumn 88, Issue 1-2, 2012, Pages 157-208

  Rubin, Timothy N ^a   Chambers, America ^b   Smyth, Padhraic ^b   Steyvers, Mark ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Irvine   (United States)

Author keywords

Dependency LDA; Document modeling; Graphical models; LDA; Multi label classification; Probabilistic generative models; Text classification; Topic models

Indexed keywords

DEPENDENCY-LDA; DOCUMENT MODELING; GENERATIVE MODEL; GRAPHICAL MODEL; LDA; MULTI-LABEL; TEXT CLASSIFICATION; TOPIC MODEL;

CLASSIFICATION (OF INFORMATION);

INFORMATION RETRIEVAL SYSTEMS;

EID: 84865237508     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-011-5272-5     Document Type: Article

References (51)

1. The EUR-Lex repository, June 2010. URL http://www.ke.tu-darmstadt.de/ resources/eurlex/eurlex.html.
2. Allwein, E. L., Schapire, R. E., & Singer, Y. (2001). Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, 1, 113-141. (Pubitemid 33738778)
3. Blei, D., & McAuliffe, J. (2008). Supervised topic models. In J. C. Platt, D. Koller, Y. Singer, & S. Roweis (Eds.), Advances in neural information processing systems 20 (pp. 121-128). Cambridge: MIT Press.
4. Blei, D. M., & Lafferty, J. D. (2005). Correlated topic models. In Advances in neural information processing systems.
5. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
6. Blei, D. M., Griffiths, T. L., & Jordan, M. I. (2010). The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 57, 7:1-7:30.
7. Cao, L., & Fei-fei, L. (2007). Spatially coherent latent topic model for concurrent object segmentation and classification. In Proceedings of IEEE International Conference in Computer Vision (ICCV).
8. Crammer, K., & Singer, Y. (2003). A family of additive online algorithms for category ranking. Journal of Machine Learning Research, 3, 1025-1058.
9. Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and roc curves. In ICML'06: proceedings of the 23rd international conference on machine learning (pp. 233-240). New York: ACM.
10. de Carvalho, A. C. P. L. F., & Freitas, A. A. (2009). A tutorial on multi-label classification techniques. In foundations of computational intelligence: Vol. 5. Studies in computational intelligence 205 (pp. 177-195). Berlin: Springer.
11. Dekel, O. Shamir, O. (2010).Multiclass-multilabel classification with more classes than examples. Journal of Machine Learning Research -Proceedings Track, 9, 137-144.
12. Druck, G., Pal, C., McCallum, A., & Zhu, X. (2007). Semi-supervised classification with hybrid generative/discriminative methods. In KDD'07: proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 280-289). New York: ACM. (Pubitemid 350229215)
13. Eyheramendy, S., Genkin, A., Ju,W.-H., Lewis, D. D., &Madigan, D. (2003). Sparse Bayesian classifiers for text categorization (Technical report). Journal of Intelligence Community Research and Development.
14. Fan, R.-E., & Lin, C.-J. (2007). A study on threshold selection for multi-label classification (Technical report). National Taiwan University.
15. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., & Lin, C.-J. (2008). LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9, 1871-1874.
16. Forman, G. (2003). An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research, 3, 1289-1305.
17. Fürnkranz, J., Hüllermeier, E., Mencía, E. L., & Brinker, K. (2008). Multilabel classification via calibrated label ranking. Machine Learning, 73(2), 133-153.
18. Ghamrawi, N., & McCallum, A. (2005). Collective multi-label classification. In CIKM'05: proceedings of the 14th ACM international conference on information and knowledge management (pp. 195-200). New York: ACM. (Pubitemid 44022130)
19. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5228-5235. (Pubitemid 38469131)
20. Har-Peled, S., Roth, D., & Zimak, D. (2002). Constraint classification: A new approach to multiclass classification and ranking (Technical report). Champaign, IL, USA.
21. Hersh, W., Buckley, C., Leone, T. J., & Hickam, D. (1994). OHSUMED: An interactive retrieval evaluation and new large test collection for research. In SIGIR'94: proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval (pp. 192-201). New York: Springer.
22. Ioannou, M., Sakkas, G., Tsoumakas, G., & Vlahavas, I. (2010). Obtaining bipartitions from score vectors for multi-label classification. In Proceedings of the 2010 22nd IEEE international conference on tools with artificial intelligence -Volume 01, ICTAI'10 (pp. 409-416). Washington: IEEE Comput. Soc. ISBN 978-0-7695-4263-8. doi:http://dx.doi.org/10.1109/ICTAI.2010. 65. URL http://dx.doi.org/10.1109/ICTAI.2010.65.
23. Japkowicz, N., & Stephen, S. (2002). The class imbalance problem: A systematic study. Intelligent Data Analysis, 6(5), 429-449.
24. Ji, S., Tang, L., Yu, S., & Ye, J. (2008). Extracting shared subspace for multi-label classification. In KDD'08: proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 381-389). New York: ACM.
25. Lacoste-Julien, S., Sha, F., & Jordan, M. I. (2008). DiscLDA: discriminative learning for dimensionality reduction and classification. In NIPS (pp. 897-904).
26. Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (2004). RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5, 361-397.
27. Liu, T.-Y., Yang, Y., Wan, H., Zeng, H.-J., Chen, Z., & Ma, W.-Y. (2005). Support vector machines classification with a very large-scale taxonomy. SIGKDD Explorations Newsletter, 7(1), 36-43.
28. Loza Mencía, E., & Fürnkranz, J. (2008a). Efficient pairwise multilabel classification for large-scale problems in the legal domain. In ECML PKDD'08: proceedings of the European conference on machine learning and knowledge discovery in databases -Part II (pp. 50-65). Berlin: Springer.
29. Loza Mencía, E., & Fürnkranz, J. (2008b). Efficient multilabel classification algorithms for large-scale problems in the legal domain. In Proceedings of the LREC 2008 workshop on semantic processing of legal texts.
30. McCallum, A. K. (1999). Multi-label text classification with a mixture model trained by EM. In AAAI 99 workshop on text learning.
31. Mimno, D., & McCallum, A. (2008). Topic models conditioned on arbitrary features with Dirichletmultinomial regression. In Proceedings of the 24th conference on uncertainty in artificial intelligence (UAI'08).
32. Mimno, D., Li, W., & McCallum, A. (2007). Mixtures of hierarchical topics with pachinko allocation. In ICML'07: proceedings of the 24th international conference on machine learning (pp. 633-640). New York: ACM.
33. Rak, R., Kurgan, L., & Reformat, M. (2005). Multi-label associative classification of medical documents from medline. In ICMLA'05: proceedings of the fourth international conference on machine learning and applications, Washington, DC, USA (pp. 177-186).
34. Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 conference on empirical methods in natural language processing, Singapore, August 2009 (pp. 248-256). Association for Computational Linguistics.
35. Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2009). Classifier chains for multi-label classification. In ECML/PKDD (2) (pp. 254-269).
36. Rifkin, R., Klautau, A. (2004). In defense of one-vs-all classification. Journal of Machine Learning Research, 5, 1532-4435.
37. Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. (2004). The author-topic model for authors and documents. In AUAI'04: proceedings of the 20th conference on uncertainty in artificial intelligence (pp. 487-494). Arlington: AUAI Press.
38. Sandhaus, E. (2008). The New York Times Annotated Corpus. Linguistic Data Consortium. Philadelphia.
39. Schneider, K.-M. (2004). On word frequency information and negative evidence in naive Bayes text classification. In España for natural language processing, EsTAL.
40. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1-47.
41. Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2004). Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101, 1566-1581.
42. Tsoumakas, G., & Katakis, I. (2007). Multi label classification: An overview. International Journal of Data Warehouse and Mining, 3(3), 1-13.
43. Tsoumakas, G., Katakis, I., & Vlahavas, I. (2009). Data mining and knowledge discovery handbook. Mining multi-label data. Berlin: Springer.
44. Ueda, N., & Saito, K. (2002). Parametric mixture models for multi-labeled text. In NIPS (pp. 721-728).
45. Wang, Y., Sabzmeydani, P., & Mori, G. (2007). Semi-latent Dirichlet allocation: A hierarchical model for human action recognition. In Proceedings of the 2nd conference on human motion: understanding, modeling, capture and animation (pp. 240-254). Berlin: Springer.
46. Yang, Y. (1999). An evaluation of statistical approaches to text categorization. Information Retrieval, 1(1-2), 69-90.
47. Yang, Y. (2001). A study of thresholding strategies for text categorization. In SIGIR'01: proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval (pp. 137-145). New York: ACM. (Pubitemid 32951066)
48. Yang, Y., Zhang, J., & Kisiel, B. (2003). A scalability analysis of classifiers in text categorization. In SIGIR' 03: proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval (pp. 96-103). New York: ACM.
49. Zhang, M.-L., & Zhang, K. (2010). Multi-label learning by exploiting label dependency. In KDD'10: proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 999-1008). New York: ACM.
50. Zhang, M.-L., Peña, J. M., & Robles, V. (2009). Feature selection for multi-label naive Bayes classification. Information Science, 179(19), 3218-3229.
51. Zhu, J., Ahmed, A., & Xing, E. P. (2009).MedLDA: maximum margin supervised topic models for regression and classification. In Proceedings of the 26th annual international conference on machine learning, ICML'09 (pp. 1257-1264). New York: ACM.