InterActive feature selection

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2005, Pages 841-846

  Raghavan, Hema ^a   Madani, Omid ^b   Jones, Rosie ^b  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

^b YAHOO INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; CLASSIFIER PERFORMANCE; FEATURE LABELING; FEATURE RELEVANCE; INTERACTIVE FEATURES; RELEVANT FEATURES; SELECTIVE SAMPLING; TEXT CATEGORIZATION;

ARTIFICIAL INTELLIGENCE; IMPORTANCE SAMPLING; TEXT PROCESSING;

EXPERIMENTS;

EID: 84880745728     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. D. Angluin. Comp. learning theory: survey and selected bibl. In Proc. 24th Annu. ACM Sympos. Theory Comput., pages 351-369, 1992.
2. Y. Baram, R. El-Yaniv, and K. Luz. Online choice of active learning algorithms. In Proc. of ICML-2003, pages 19-26, 2003.
3. E. B. Baum and K. Lang. Query learning can work poorly when human oracle is used. In Intern Joint Conf in Neural Netwroks, 1992.
4. Philip Beineke, Trevor Hastie, and Shivakumar Vaithyanathan. The sentimental factor: Improving review classification via human-provided information. In Proc. of ACL'04, Main Volume, pages 263-270, Barcelona, Spain, July 2004.
5. J. Brank, M. Grobelnik, N. Milic-Frayling, and D. Mladenic. Feature selection using linear support vector machines. Technical report, Microsoft Research, 2002.
6. D. A. Cohn, L. Atlas, and R. E. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201-221, 1994.
7. W. B. Croft and R. Das. Experiments with query acquisition and use in document retrieval systems. In SIGIR '90, 1990.
8. S. Godbole, A. Harpale, S. Sarawagi, and S. Chakrabarti. Document classification through interactive supervision of document and term labels. In PKDD, pages 185-196, 2004.
9. D. D. Lewis and J. Catlett. Heterogeneous uncertainty sampling for supervised learning. In Proc. of ICML-94, pages 148-156, 1994.
10. A. K. McCallum. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. http://www.cs.cmu.edu/ mccallum/bow, 1996.
11. M. J. Pazzani and D. Kibler. The role of prior knowledge in inductive learning. Machine Learning, 9, 54-97., 9, 1992.
12. Hema Raghavan, Omid Madani, and Rosie Jones. Interactive feature selection. Technical report, University of Massachusetts, Amherst, 2005.
13. T. G. Rose, M. Stevenson, and M. Whitehead. The reuters corpus vol. 1 - from yesterday's news to tomorrow's language resources. In Inter. Conf. on Lang. Resources and Evaluation, 2002.
14. G. Salton. Automatic Information Organization and Retrieval. McGraw Hill, 1968.
15. R. Schapire, M. Rochery, M. Rahim, and N. Gupta. Incorporating prior knowledge into boosting. In ICML, 2002.
16. B. Scholkopf and A. Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
17. F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surverys, 2002.
18. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. J. Mach. Learn. Res., 2:45-66, 2002.
19. X. Wu and R. Srihari. Incorporating prior knowledge with weighted margin support vector machines. In Proc. of KDD, 2004.