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

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タイトル別名
  • A Case Study for Regulatory Relations of <I>S. cerevisiae</I> Genes in MEDLINE

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説明

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.

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詳細情報 詳細情報について

  • CRID
    1390001204491316096
  • NII論文ID
    130003997761
  • DOI
    10.11234/gi1990.9.91
  • ISSN
    2185842X
    09199454
  • PubMed
    11072325
  • 本文言語コード
    en
  • 資料種別
    journal article
  • データソース種別
    • JaLC
    • PubMed
    • CiNii Articles
  • 抄録ライセンスフラグ
    使用不可

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