Intelligent GP fusion from multiple sources for text classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Zhang:2005:IGF,
  author =       "Baoping Zhang and Yuxin Chen and Weiguo Fan and 
                 Edward A. Fox and Marcos Goncalves and Marco Cristo and 
                 Pavel Calado",
  title =        "Intelligent {GP} fusion from multiple sources for text
                 classification",
  booktitle =    "Proceedings of the 14th {ACM} international Conference
                 on Information and Knowledge Management",
  year =         "2005",
  month =        oct # " 31-" # nov # " 5",
  address =      "Bremen, Germany",
  publisher =    "ACM Press",
  organisation = "ACM: Association for Computing Machinery SIGIR: ACM
                 Special Interest Group on Information Retrieval",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://homepages.dcc.ufmg.br/~mgoncalv/DLCourse/f302-zhang.pdf",
  DOI =          "doi:10.1145/1099554.1099688",
  size =         "8 pages",
  abstract =     "This paper shows 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 information of the collection, and three
                 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
                 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 GA or other fusion techniques.",
  notes =        "CIKM'05",
}

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

Citations