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Scalable Improved Quick Reduct: Sample Based

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Rough Sets and Knowledge Technology (RSKT 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7414))

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Abstract

This paper develops an iterative sample based Improved Quick Reduct algorithm with Information Gain heuristic approach for recommending a quality reduct for large decision tables. The Methodology and its performance have been demonstrated by considering large datasets. It is recommended to use roughly 5 to 10% data size for obtaining an apt reduct.

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Author information

Authors and Affiliations

  1. University of Hyderabad, Hyderabad, India

    P. S. V. S. Sai Prasad & C. Raghavendra Rao

Authors
  1. P. S. V. S. Sai Prasad
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  2. C. Raghavendra Rao
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Editor information

Editors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, 610031, Chengdu, P.R. China

    Tianrui Li

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

    Hung Son Nguyen

  3. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

    Guoyin Wang

  4. Department of Electrical Engineering and Computer Science, University of Kansas, 66045 - 7621, Lawrence, KS, USA

    Jerzy Grzymala-Busse

  5. Department of Computing and Software, McMaster University, L8S 4K1, Hamilton, Ontario, Canada

    Ryszard Janicki

  6. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  7. Software School, Dalian University of Technology, Dalian, China

    Hong Yu

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© 2012 Springer-Verlag Berlin Heidelberg

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Sai Prasad, P.S.V.S., Raghavendra Rao, C. (2012). Scalable Improved Quick Reduct: Sample Based. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_5

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  • DOI: https://doi.org/10.1007/978-3-642-31900-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31899-3

  • Online ISBN: 978-3-642-31900-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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