Exceptions as Chance for Computational Chance Discovery

Authors:
Akinori Abe;Norihiro Hagita;Michiko Furutani;Yoshiyuki Furutani;Rumiko Matsuoka
Affiliations:
International Research and Educational Institute for Integrated Medical Science (IREIIMS), Tokyo Women's Medical University, Tokyo, Japan 162-8666 and ATR Knowledge Science Laboratories, ,;International Research and Educational Institute for Integrated Medical Science (IREIIMS), Tokyo Women's Medical University, Tokyo, Japan 162-8666 and ATR Intelligent Robotics and Communication La ...;International Research and Educational Institute for Integrated Medical Science (IREIIMS), Tokyo Women's Medical University, Tokyo, Japan 162-8666;International Research and Educational Institute for Integrated Medical Science (IREIIMS), Tokyo Women's Medical University, Tokyo, Japan 162-8666;International Research and Educational Institute for Integrated Medical Science (IREIIMS), Tokyo Women's Medical University, Tokyo, Japan 162-8666
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Year:
2008

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Abstract

In this paper, we analyze clinical data to model relationships between clinical data and health levels. During analyses of data, we discovered models which are important for determining health levels but cannot be extracted during machine learning process. We regard such models as chance and propose an interactive determination of such models. The obtained models can be referred to when standard models cannot correctly explain certain individual health levels.