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Two Novel Decomposition Approaches for Knowledge Acquisition Model

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Rough Sets (IJCRS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10314))

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Abstract

Knowledge acquisition, one of essential issues for data mining, has always been a hot topic due to the explosive growth of information. However, when handling large-scale data, many current knowledge acquisition algorithms based on rough set theory are inefficient. In this paper, novel decomposition approaches for knowledge acquisition are put forward. The principal of decomposition is to split a complex problem in several problems. Those problems are composed of a master-problem and several sub-problems which are simpler, more manageable and more solvable by using existing induction methods, then joining them together in order to solve the original problem. Compared with some traditional algorithms, the efficiency of the proposed approaches can be illustrated by experiments with standard datasets from UCI database.

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Acknowledgements

This paper is supported by the National Social Science Fund (Granted No. 13CFX049), Shanghai University Young Teacher Training Program (Granted No. hdzf10008) and the Research Fund for East China University of Political science and Law (Granted No. 11H2K034).

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Authors and Affiliations

  1. Department of Information Science and Technology, East China University of Political Science and Law, Shanghai, 201620, People’s Republic of China

    Na Jiao

Authors
  1. Na Jiao
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Correspondence to Na Jiao .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Jiao, N. (2017). Two Novel Decomposition Approaches for Knowledge Acquisition Model. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10314. Springer, Cham. https://doi.org/10.1007/978-3-319-60840-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-60840-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60839-6

  • Online ISBN: 978-3-319-60840-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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