• DocumentCode
    2020796
  • Title

    Research of Text Classification Technology based on Genetic Annealing Algorithm

  • Author

    Zhen-fang, Zhu ; Pei-yu, Liu ; Ran, Lu

  • Author_Institution
    Shandong Normal Univ., Jinan
  • Volume
    1
  • fYear
    2008
  • fDate
    17-18 Oct. 2008
  • Firstpage
    265
  • Lastpage
    269
  • Abstract
    Text classification has received extensive attention in recent years, which is an important means of data mining. This paper analyzed basic theory and general structure of text classification, given a text classification method based on improved genetic algorithms, introduced simulated annealing mechanism of genetic algorithm to solve the precocious easy, local optimum, and so on, using the Roocchio feedback model to achieve feedback and self-learning of text classification. Experiment, we used this method to achieve a text classification system and the traditional KNN classification method were compared, results showed that the training methods for text classification has good accuracy and recall rate.
  • Keywords
    feedback; genetic algorithms; simulated annealing; text analysis; Roocchio feedback model; genetic annealing algorithm; simulated annealing mechanism; text classification technology; Algorithm design and analysis; Annealing; Classification algorithms; Computational intelligence; Data mining; Feedback; Genetic algorithms; Support vector machine classification; Support vector machines; Text categorization; GA; Text classification; feedback; simulated annealing mechanism;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Design, 2008. ISCID '08. International Symposium on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3311-7
  • Type

    conf

  • DOI
    10.1109/ISCID.2008.83
  • Filename
    4725605