Intelligent fusion of structural and citation-based evidence for text classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Zhang05c,
  author =       "Baoping Zhang and Yuxin Chen and Weiguo Fan and 
                 Edward A. Fox and Marcos Andre Goncalves and Marco Cristo and 
                 Pavel Calado",
  title =        "Intelligent fusion of structural and citation-based
                 evidence for text classification",
  booktitle =    "Proceedings of the 28th Annual International ACM SIGIR
                 Conference on Research and Development in Information
                 Retrieval",
  pages =        "667--668",
  year =         "2005",
  address =      "Salvador, Brazil",
  publisher_address = "New York, NY, USA",
  month =        aug # " 15-19",
  organisation = "SIGIR: ACM Special Interest Group on Information
                 Retrieval",
  publisher =    "ACM Press",
  keywords =     "genetic algorithms, genetic programming, Poster",
  ISBN =         "1-59593-034-5",
  size =         "2 pages",
  copyright =    "Copyright is held by the author/owner.",
  MRnumber =     "C.IR.05.667",
  DOI =          "doi:10.1145/1076034.1076181",
  abstract =     "This paper shows how different measures of similarity
                 derived from the citation information and the
                 structural content (e.g., title, abstract) of the
                 collection can be fused to improve classification
                 effectiveness. To discover the best fusion framework,
                 we apply Genetic Programming (GP) techniques. Our
                 experiments with the ACM Computing Classification
                 Scheme, using documents from the ACM Digital Library,
                 indicate that GP can discover similarity functions
                 superior to those based solely on a single type of
                 evidence. Effectiveness of the similarity functions
                 discovered through simple majority voting is better
                 than that of content-based as well as combination-based
                 Support Vector Machine classifiers. Experiments also
                 were conducted to compare the performance between GP
                 techniques and other fusion techniques such as Genetic
                 Algorithms (GA) and linear fusion. Empirical results
                 show that GP was able to discover better similarity
                 functions than other fusion techniques.",
  notes =        "See also \cite{Zhang05cTR}",
}

Genetic Programming entries for Baoping Zhang Yuxin (Jerry) Chen Weiguo Fan Edward A Fox Marcos Andre Goncalves Marco Cristo Pavel Pereira Calado

Citations