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Chance discovery and learning minority classes

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Abstract

Chances are viewed in chance discovery as events/situations with significant impact on human decision making. In this research context we are particularly interested in a subset of chances that are unexpected or contradictory with human common knowledge, and the human role that we consider as an essential factor in finding such chances. We first introduce the method LUPC that can learn minority classes from large unbalanced datasets. With its visualization tools as well its exclusive and inclusive constraints, LUPC allows the user to actively participate in and to incorporate background knowledge in the chance discovery process. We then present case studies in which LUPC is used to support the user in discovering significant unexpected chances from stomach cancer and hepatitis databases.

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Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology, 923-1292, Tatsunokuchi, Ishikawa, Japan

    Tu Bao Ho & Duc Dung Nguyen

Authors
  1. Tu Bao Ho
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  2. Duc Dung Nguyen
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Correspondence to Tu Bao Ho.

Additional information

Tu Bao Ho: He received a B. Eng. degree from Hanoi University of Technology in 1978, M.S. and Ph.D. degrees from University Paris 6, in 1984 and 1987. He is currently professor at School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST). His research interests include machine learning, knowledge-based systems and knowledge discovery and data mining.

Duc Dung Nguyen: He received a B.A. degree from Hanoi University of Pedagogy in 1993. He is currently a master student at School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST). His research interests include pattern recognition and knowledge discovery and data mining.

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Ho, T.B., Nguyen, D.D. Chance discovery and learning minority classes. New Gener Comput 21, 149–161 (2003). https://doi.org/10.1007/BF03037632

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  • DOI: https://doi.org/10.1007/BF03037632

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