Intelligent Fusion of Structural and Citation-Based Evidence for Text Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @TechReport{Zhang05cTR,
-
author = "Baoping Zhang and Marcos Andre Goncalves and
Weiguo Fan and Yuxin Chen and Edward A. Fox and
Pavel Calado and Marco Cristo",
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title = "Intelligent Fusion of Structural and Citation-Based
Evidence for Text Classification",
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institution = "Computer Science, Virginia Tech",
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year = "2004",
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number = "TR-04-16",
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keywords = "genetic algorithms, genetic programming,
Classification, document similarity, citation analysis,
Computer Science, Information Retrieval, Digital
Libraries",
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URL = "http://eprints.cs.vt.edu/archive/00000693/01/GP5.pdf",
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size = "9 pages",
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abstract = "This paper investigates how citation-based information
and structural content (e.g., title, abstract) can be
combined to improve classification of text documents
into predefined categories. We evaluate different
measures of similarity, five derived from the citation
structure of the collection, and three measures derived
from the structural content, and determine how they can
be fused to improve classification effectiveness. To
discover the best fusion framework, we apply Genetic
Programming (GP) techniques. Our empirical experiments
using documents from the ACM digital library and the
ACM classification scheme show that we can discover
similarity functions that work better than any evidence
in isolation and whose combined performance through a
simple majority voting is comparable to that of Support
Vector Machine classifiers.",
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notes = "See also \cite{Zhang05c}
ID Code: 693 Deposited By: Administrator, Eprints
Deposited On: 09 September 2005
Site Administrator: eprints@cs.vt.edu",
- }
Genetic Programming entries for
Baoping Zhang
Marcos Andre Goncalves
Weiguo Fan
Yuxin (Jerry) Chen
Edward A Fox
Pavel Pereira Calado
Marco Cristo
Citations