Multiple-Instance Learning Based Heuristics for Mining Chemical Compound Structure

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Author(s)

Abstract

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.

Journal

  • IEICE technical report. Artificial intelligence and knowledge-based processing

    IEICE technical report. Artificial intelligence and knowledge-based processing 104(487), 7-12, 2004-12-06

    The Institute of Electronics, Information and Communication Engineers

References:  5

Codes

  • NII Article ID (NAID)
    110003205585
  • NII NACSIS-CAT ID (NCID)
    AN10013061
  • Text Lang
    ENG
  • Article Type
    ART
  • ISSN
    09135685
  • NDL Article ID
    7222473
  • NDL Source Classification
    ZN33(科学技術--電気工学・電気機械工業--電子工学・電気通信)
  • NDL Call No.
    Z16-940
  • Data Source
    CJP  NDL  NII-ELS 
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