A Machine Learning Approach to Reducing the Work of Experts in Article Selection from Database

Bibliographic Information

Other Title
  • A Case Study for Regulatory Relations of <I>S. cerevisiae</I> Genes in MEDLINE

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Description

We consider the problem of selecting the articles of experts' interest from a literature database with the assistance of a machine learning system. For this purpose, we propose the rough reading strategy which combines the experts' knowledge with the machine learning system. For the articles converted through the rough reading strategy, we employ the learning system BONSAI and apply it for discovering rules which may reduce the work of experts in selecting the articles. Furthermore, we devise an algorithm which iterates the above procedure until almost all records of experts' interest are selected. Experimental results by using the articles from Cell show that almost all records of experts' interest are selected while reducing the works of experts drastically.

Journal

  • Genome Informatics

    Genome Informatics 9 91-101, 1998

    Japanese Society for Bioinformatics

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Details 詳細情報について

  • CRID
    1390001204491316096
  • NII Article ID
    130003997761
  • DOI
    10.11234/gi1990.9.91
  • ISSN
    2185842X
    09199454
  • PubMed
    11072325
  • Text Lang
    en
  • Article Type
    journal article
  • Data Source
    • JaLC
    • PubMed
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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