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Fuzzy Rough Decision Trees

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Rough Sets and Current Trends in Computing (RSCTC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7413))

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Abstract

Fuzzy rough sets are widely studied and applied in the domain of machine learning and data mining these years. In this work, this theory is used to design a fuzzy rough decision tree algorithm which can be used to deal with the cognitive uncertainties such as vagueness and ambiguity associated with human thinking and perception. In our algorithm, both selecting nodes and splitting branches in constructing the tree are based on fuzzy rough set theory. Especially, the current branching point is determined by pureness of the two branches, where the pureness is based on fuzzy lower approximation. The comparison results show that our decision tree algorithm is equivalent to or outperforms some popular decision tree algorithms.

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Author information

Authors and Affiliations

  1. Northeastern University, Shenyang, 110819, P.R. China

    Shuang An

  2. Tianjin University, Tianjin, 300072, P.R. China

    Shuang An & Qinghua Hu

Authors
  1. Shuang An
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  2. Qinghua Hu
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina, S4S 0A2, Regina, SK, Canada

    JingTao Yao

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

    Yan Yang

  3. Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland

    Roman Słowiński

  4. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco

  5. School of Management and Engineering, Nanjing University, 210093, Nanjing, Jiangsu, P.R. China

    Huaxiong Li

  6. Machine Intelligence Unit, Indian Statistical Institute (ISI), 700108, Kolkata, India

    Sushmita Mitra

  7. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland

    Lech Polkowski

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© 2012 Springer-Verlag Berlin Heidelberg

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An, S., Hu, Q. (2012). Fuzzy Rough Decision Trees. In: Yao, J., et al. Rough Sets and Current Trends in Computing. RSCTC 2012. Lecture Notes in Computer Science(), vol 7413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32115-3_47

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  • DOI: https://doi.org/10.1007/978-3-642-32115-3_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32114-6

  • Online ISBN: 978-3-642-32115-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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