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Acquisition of Knowledge in the Form of Fuzzy Rules for Cases Classification

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Data Mining and Big Data (DMBD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10387))

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Abstract

We consider an approach to automatic knowledge acquisition through machine learning based on integration of two basic paradigms of reasoning – case-based and rule-based reasoning. Case-based reasoning allows to use high-performance database technology for storing and accumulating cases, while rule-based reasoning is the most developed technology for creating declarative knowledge on the basis of strong logical inference. We also propose an improvement of classification algorithm through extraction of fuzzy rules from cases. We have obtained higher classification accuracy for various membership functions and for sequentially reducing amount of training sample by application of special strategies for expanding the scope of fuzzy rules in the control sample classification.

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Acknowledgments

The reported study was funded by Russian Ministry of Education and Science, according to the research project No. 2.2327.2017/4.6

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Authors and Affiliations

  1. Novosibirsk State Technical University, Novosibirsk, Russia

    Tatiana Avdeenko & Ekaterina Makarova

Authors
  1. Tatiana Avdeenko
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  2. Ekaterina Makarova
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Corresponding author

Correspondence to Tatiana Avdeenko .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Avdeenko, T., Makarova, E. (2017). Acquisition of Knowledge in the Form of Fuzzy Rules for Cases Classification. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_53

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  • DOI: https://doi.org/10.1007/978-3-319-61845-6_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61844-9

  • Online ISBN: 978-3-319-61845-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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