Multiple-Instance Learning Based Heuristics for Mining Chemical Compound Structure

この論文をさがす

著者

抄録

Inductive Logic Programming (ILP) is a combination of inductive learning and first-order logic aiming to learn first-order hypotheses from training examples. ILP has a serious bottleneck in an intractably enormous hypothesis search space. This makes existing approaches perform poorly on large-scale real-world datasets. In this research, we propose a technique to make the system handle an enormous search space efficiently by deriving qualitative information into search heuristics. Currently, heuristic functions used in ILP systems are based only on quantitative information, e.g. number of examples covered and length of candidates. We focus on a kind of data consisting of several parts. The approach aims to find hypotheses describing each class by using both individual and relational features of parts. The data can be found in denoting chemical compound structure for Structure-Activity Relationship. Studies (SAR). We apply the proposed method to extract rules describing chemical activity from their structures. The experiments are conducted on a real-world dataset. The results are compared to existing ILP methods using ten-fold cross validation.

収録刊行物

  • 電子情報通信学会技術研究報告. AI, 人工知能と知識処理

    電子情報通信学会技術研究報告. AI, 人工知能と知識処理 104(487), 7-12, 2004-12-06

    一般社団法人電子情報通信学会

参考文献:  5件中 1-5件 を表示

各種コード

  • NII論文ID(NAID)
    110003205585
  • NII書誌ID(NCID)
    AN10013061
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    09135685
  • NDL 記事登録ID
    7222473
  • NDL 雑誌分類
    ZN33(科学技術--電気工学・電気機械工業--電子工学・電気通信)
  • NDL 請求記号
    Z16-940
  • データ提供元
    CJP書誌  NDL  NII-ELS 
ページトップへ