Active learning for hierarchical text classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7301 LNAI, Issue PART 1, 2012, Pages 14-25

  Li, Xiao ^a   Kuang, Da ^a   Ling, Charles X ^a  

^a UNIVERSITY OF WESTERN ONTARIO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; HIERARCHICAL CLASSIFICATION; HIERARCHICAL RELATIONS; IN-BUILDINGS; REAL-WORLD APPLICATION; TECHNICAL CHALLENGES; TEXT CLASSIFICATION; TOPIC CLASSIFICATION; TRAINING EXAMPLE; USER FEEDBACK; WEB-PAGE;

CLASSIFICATION (OF INFORMATION);

DATA MINING;

EID: 84861449161     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-30217-6_2     Document Type: Conference Paper

References (20)

1. Ceci, M., Malerba, D.: Classifying web documents in a hierarchy of categories: a comprehensive study. J. Intell. Inf. Syst. 28, 37-78 (2007)
2. D'Alessio, S., Murray, K., Schiaffino, R., Kershenbaum, A.: The effect of using hierarchical classifiers in text categorization. In: RIAO 2000, pp. 302-313 (2000)
3. Daraselia, N., Yuryev, A., Egorov, S., Mazo, I., Ispolatov, I.: Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks. BMC Bioinformatics 8(1), 243 (2007)
4. Donmez, P., Carbonell, J.G.: Proactive learning: cost-sensitive active learning with multiple imperfect oracles. In: CIKM 2008, pp. 619-628 (2008)
5. Esuli, A., Sebastiani, F.: Active Learning Strategies for Multi-Label Text Classification. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 102-113. Springer, Heidelberg (2009)
6. Fagni, T., Sebastiani, F.: Selecting negative examples for hierarchical text classification: An experimental comparison. J. Am. Soc. Inf. Sci. Technol. 61, 2256-2265 (2010)
7. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification. The Journal of Machine Learning Research 9, 1871-1874 (2008)
8. Lam, W., Ho, C.Y.: Using a generalized instance set for automatic text categorization. In: SIGIR 1998, pp. 81-89 (1998)
9. Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361-397 (2004)
10. Nowak, S., Rüger, S.: How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation. In: MIR 2010, pp. 557-566 (2010)
11. Platt, J.C.: Probabilistic outputs for support vector machines. In: Advances in Large Margin Classifiers, pp. 61-74 (1999)
12. Roy, N., McCallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: ICML 2001, pp. 441-448 (2001)
13. Ruiz, M.E., Srinivasan, P.: Hierarchical neural networks for text categorization (poster abstract). In: SIGIR 1999, pp. 281-282 (1999)
14. Silla Jr., C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Discov. 22, 31-72 (2011)
15. Sun, A., Lim, E.-P.: Hierarchical text classification and evaluation. In: ICDM 2001, pp. 521-528 (2001)
16. Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45-66 (2002)
17. Verspoor, K., Cohn, J., Mniszewski, S., Joslyn, C.: Categorization approach to automated ontological function annotation. In: Protein Science, pp. 1544-1549 (2006)
18. Xu, Z., Yu, K., Tresp, V., Xu, X., Wang, J.: Representative Sampling for Text Classification Using Support Vector Machines. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633, pp. 393-407. Springer, Heidelberg (2003)
19. Xue, G.R., Xing, D., Yang, Q., Yu, Y.: Deep classification in large-scale text hierarchies. In: SIGIR 2008, pp. 619-626 (2008)
20. Yang, B., Sun, J.T., Wang, T., Chen, Z.: Effective multi-label active learning for text classification. In: KDD 2009, pp. 917-926 (2009)