Classifier Stacking and Voting for Text Filtering

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This article discusses classifier stacking and voting for text filtering.

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6 p.

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Mihalcea, Rada, 1974- November 2002.

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This paper is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Engineering to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 86 times . More information about this paper can be viewed below.

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This article discusses classifier stacking and voting for text filtering.

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6 p.

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Abstract: This paper summarizes the approach and the results of the TextCat system participating in the Filtering track in the Text Retrieval Conference 2002. The system relies primarily on statistical methods, and was designed with the main purpose of having a backbone system in which we can further integrate semantic components, and evaluate their relative performance as compared to traditional statistical approaches. They system is therefore simple, and is based on techniques for keywords extraction, and various classifier combinations including stacking and voting. TextCat participated in the Batch and Routing tasks. In the Batch task, it achieved a score of 39.02% normalized utility, and 26.37% F-measure respectively, averaged over all topics. The averaged uninterpolated precision for our best routing submission was 14.16%.

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  • Eleventh Text Retrieval Conference (TREC), 2002, Gaithersburg, Maryland, United States

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  • November 2002

Added to The UNT Digital Library

  • Jan. 31, 2011, 2:01 p.m.

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  • March 27, 2014, 11:35 a.m.

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Mihalcea, Rada, 1974-. Classifier Stacking and Voting for Text Filtering, paper, November 2002; [Gaithersburg, Maryland]. (digital.library.unt.edu/ark:/67531/metadc30942/: accessed September 26, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.