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Combining feature selectors for text classification
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Source Conference on Information and Knowledge Management archive
Proceedings of the 15th ACM international conference on Information and knowledge management table of contents
Arlington, Virginia, USA
POSTER SESSION: Posters table of contents
Pages: 798 - 799  
Year of Publication: 2006
ISBN:1-59593-433-2
Authors
J. Olsson Scott Olsson  University of Maryland, College Park, Maryland
Douglas W. Oard  University of Maryland, College Park, Maryland
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
Publisher
ACM  New York, NY, USA
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ABSTRACT

We introduce several methods of combining feature selectors for text classification. Results from a large investigation of these combinations are summarized. Easily constructed combinations of feature selectors are shown to improve peak R-precision and F1 at statistically significant levels.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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J. S. Olsson and D. W. Oard. Exploring feature selection for multi-label text classification using ranked retrieval measures. University of Maryland CS technical report, UMIACS-TR-2006-41, 2006.
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Collaborative Colleagues:
J. Olsson Scott Olsson: colleagues
Douglas W. Oard: colleagues