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Variable Neighbourhood Iterated Improvement Search Algorithm for Attribute Reduction Problems

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Book cover Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

Attribute reduction is one of the main contributions in Rough Set Theory (RST) that tries to find all possible reducts by eliminating redundant attributes while maintaining the information of the problem in hand. In this paper, we propose a meta-heuristic approach called a Variable Neighbourhood Iterated Improvement Search (VNS-IIS) algorithm for attribute reduction. It is a combination of the variable neighbourhood search with the iterated search algorithm where two local search algorithms i.e. a random iterated local search and a sequential iterated local search algorithm are employed in a parallel strategy. In VNS-IIS, an improved solution will always be accepted. The proposed method has been tested on the 13 well-known datasets that are available in the UCI machine learning repository. Experimental results show that the VNS-IIS is able to obtain competitive results when compared with other approaches mentioned in the literature in terms of minimal reducts.

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Author information

Authors and Affiliations

  1. Data Mining and Optimisation Research Group (DMO), Centre for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Yahya Z. Arajy, Salwani Abdullah & Saif Kifah

Authors
  1. Yahya Z. Arajy
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  2. Salwani Abdullah
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  3. Saif Kifah
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Arajy, Y.Z., Abdullah, S., Kifah, S. (2014). Variable Neighbourhood Iterated Improvement Search Algorithm for Attribute Reduction Problems. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_47

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_47

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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