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Advances in Machine Learning II pp 445–466Cite as

Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 263))

Abstract

Multi-knowledge extraction is significant for many real-world applications. The nature inspired population-based reduction approaches are attractive to find multiple reducts in the decision systems, which could be applied to generate multi-knowledge and to improve decision accuracy. In this Chapter, we introduce two nature inspired population-based computational optimization techniques namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for rough set reduction and multi-knowledge extraction. A Multi-Swarm Synergetic Optimization (MSSO) algorithm is presented for rough set reduction and multi-knowledge extraction. In the MSSO approach, different individuals encodes different reducts. The proposed approach discovers the best feature combinations in an efficient way to observe the change of positive region as the particles proceed throughout the search space. We also attempt to theoretically prove that the multi-swarm synergetic optimization algorithm converges with a probability of 1 towards the global optimal. The performance of the proposed approach is evaluated and compared with Standard Particle Swarm Optimization (SPSO) and Genetic Algorithms (GA). Empirical results illustrate that the approach can be applied for multiple reduct problems and multi-knowledge extraction very effectively.

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Author information

Authors and Affiliations

  1. School of Information Science and Technology, Dalian Maritime University, 116026, Dalian, China

    Hongbo Liu

  2. Centre for Quantifiable Quality of Service in Communication Systems, Norwegian University of Science and Technology, Trondheim, Norway

    Ajith Abraham

  3. School of Electronic and Information Engineering, Dalian University of Technology, Dalian, 116023, China

    Hongbo Liu & Benxian Yue

Authors
  1. Hongbo Liu
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  2. Ajith Abraham
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  3. Benxian Yue
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Editor information

Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237, Warsaw, Poland

    Jacek Koronacki  & Sławomir T. Wierzchoń  & 

  2. Woodward Hall 430C, University of North Carolina, 9201 University City Blvd., N.C. 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Systems Research Institute, Polish Academy of Sciences, ul.Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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Liu, H., Abraham, A., Yue, B. (2010). Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning II. Studies in Computational Intelligence, vol 263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05179-1_21

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  • DOI: https://doi.org/10.1007/978-3-642-05179-1_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05178-4

  • Online ISBN: 978-3-642-05179-1

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