Rough Sets and Knowledge Technology

6th International Conference, RSKT 2011, Banff, Canada, October 9-12, 2011. Proceedings

  • JingTao Yao
  • Sheela Ramanna
  • Guoyin Wang
  • Zbigniew Suraj
Conference proceedings RSKT 2011
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6954)

Table of contents

  1. Front Matter
  2. Keynote Papers

  3. Invited Tutorial

  4. Attribute Reduction and Feature Selection

  5. Generalized Rough Set Models

    1. Xinwei Zheng, Jian-Hua Dai
      Pages 120-125

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 6th International Conference on Rough Sets and Knowledge Technology, RSKT 2011, held in Banff, Canada, in September 2011.
The 89 revised full papers presented together with 3 keynote lectures and 1 invited tutorial session were carefully reviewed and selected from 229 submissions. The papers are organized in topical sections on attribute reduction and feature selection, generalized rough set models, machine learning with rough and hybrid techniques, knowledge technology and intelligent systems and applications.

Keywords

complex networks decision support systems fuzzy rough sets quality of service (QoS) similarity measure

Editors and affiliations

  • JingTao Yao
    • 1
  • Sheela Ramanna
    • 2
  • Guoyin Wang
    • 3
  • Zbigniew Suraj
    • 4
  1. 1.Department of Computer ScienceUniversity of Regina, ReginaSaskatchewanCanada
  2. 2.Department of Applied Computer ScienceUniversity of WinnipegWinnipegCanada
  3. 3.Institute of Computer Science & TechnologyChongqing University of Posts and TelecommunicationsChongqingP.R. China
  4. 4.Chair of Computer ScienceUniversity of RzeszówPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-24425-4
  • Copyright Information Springer-Verlag GmbH Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-24424-7
  • Online ISBN 978-3-642-24425-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349