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A New Heuristic Feature Selection Algorithm Based on Rough Sets

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Advanced Intelligent Computing Theories and Applications (ICIC 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 93))

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Abstract

A heuristic algorithm of reduct computation for feature selection in data mining is proposed in this paper, which aims at reducing the number of irrelevant and redundant features. This algorithm is based on the modified dependency degree formula. The advantage of this algorithm is that it can find the optimal reduct set for feature selection with less time complexity in most cases. To test the validity and generality of this algorithm, experimental results with 7 data sets from UCI Machine Learning Repository are given.

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Author information

Authors and Affiliations

  1. College of Mathematics, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China

    Hua Zhao & Keyun Qin

  2. College of Logistics, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China

    Xiaoping Qiu

Authors
  1. Hua Zhao
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  2. Keyun Qin
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  3. Xiaoping Qiu
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui,, China

    De-Shuang Huang

  2. School of Computing and Intelligent Systems, University of Ulster at Magee Campus, BT48 7JL, Derry, Northern Ireland, UK

    Martin McGinnity

  3. Laboratoire LITIS, Université de Rouen, 76800, Saint Etienne du Rouvray, France

    Laurent Heutte

  4. Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada

    Xiao-Ping Zhang

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© 2010 Springer-Verlag Berlin Heidelberg

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Zhao, H., Qin, K., Qiu, X. (2010). A New Heuristic Feature Selection Algorithm Based on Rough Sets. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_20

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  • DOI: https://doi.org/10.1007/978-3-642-14831-6_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14830-9

  • Online ISBN: 978-3-642-14831-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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