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A Novel Discretizer for Knowledge Discovery Approaches Based on Rough Sets

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Rough Sets and Knowledge Technology (RSKT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4062))

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Abstract

Knowledge discovery approaches based on rough sets have successful application in machine learning and data mining. As these approaches are good at dealing with discrete values, a discretizer is required when the approaches are applied to continuous attributes. In this paper, a novel adaptive discretizer based on a statistical distribution index is proposed to preprocess continuous valued attributes in an instance information system, so that the knowledge discovery approaches based on rough sets can reach a high decision accuracy. The experimental results on benchmark data sets show that the proposed discretizer is able to improve the decision accuracy.

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Author information

Authors and Affiliations

  1. School of Physics and OptoElectronic Technology, Fujian Normal University, Fujian, Fuzhou, 350007, China

    Qingxiang Wu & Jianyong Cai

  2. School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Londonderry, N.Ireland, BT48 7JL, UK

    Qingxiang Wu, Girijesh Prasad & T. M. McGinnity

  3. School of Computer Science, Queens University, Belfast, UK

    Qingxiang Wu, David Bell & Jiwen Guan

Authors
  1. Qingxiang Wu
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  2. Jianyong Cai
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  3. Girijesh Prasad
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  4. T. M. McGinnity
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  5. David Bell
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  6. Jiwen Guan
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Editor information

Editors and Affiliations

  1. College of Computer Science and Technology, Chongqing, University of Posts and Telecommunication, 400065, Chongqing, P.R. China

    Guo-Ying Wang

  2. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  4. Department of Computer Science, University of Regina, Regina,, S4S 0A2, Saskatchewan, Canada

    Yiyu Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Wu, Q., Cai, J., Prasad, G., McGinnity, T.M., Bell, D., Guan, J. (2006). A Novel Discretizer for Knowledge Discovery Approaches Based on Rough Sets. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_35

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  • DOI: https://doi.org/10.1007/11795131_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36297-5

  • Online ISBN: 978-3-540-36299-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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