Intelligent Fusion of Structural and Citation-Based Evidence for Text Classification

Simple item page
dc.contributor.authorZhang, Baopingen
dc.contributor.authorGoncalves, Marcos A.en
dc.contributor.authorFan, Weiguoen
dc.contributor.authorChen, Yuxinen
dc.contributor.authorFox, Edward A.en
dc.contributor.authorCalado, Pavelen
dc.contributor.authorCristo, Marcoen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2013-06-19T14:36:18Zen
dc.date.available2013-06-19T14:36:18Zen
dc.date.issued2004en
dc.description.abstractThis 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.en
dc.format.mimetypeapplication/pdfen
dc.identifierhttp://eprints.cs.vt.edu/archive/00000693/en
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00000693/01/GP5.pdfen
dc.identifier.trnumberTR-04-16en
dc.identifier.urihttp://hdl.handle.net/10919/20156en
dc.language.isoenen
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectInformation retrievalen
dc.subjectDigital librariesen
dc.titleIntelligent Fusion of Structural and Citation-Based Evidence for Text Classificationen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
GP5.pdf
Size:
123.53 KB
Format:
Adobe Portable Document Format
Download

Collections

Computer Science Technical Reports