Soft Computing

, Volume 21, Issue 23, pp 6907–6918 | Cite as

Neighborhood rough set reduction with fish swarm algorithm

  • Yumin ChenEmail author
  • Zhiqiang Zeng
  • Junwen Lu


Feature reduction refers to the problem of deleting those input features that are less predictive of a given outcome; a problem encountered in many areas such as pattern recognition, machine learning and data mining. In particular, it has been successfully applied in tasks that involve datasets containing huge numbers of features. Rough set theory has been used as such a data set preprocessor with much success, but current methods are inadequate at solving the problem of numerical feature reduction. As the classical rough set model can just be used to evaluate categorical features, we introduce a neighborhood rough set model to deal with numerical datasets by defining a neighborhood relation. However, this method is still not enough to find the optimal subsets regularly. In this paper, we propose a new feature reduction mechanism based on fish swarm algorithm (FSA) in an attempt to polish up this. The method is then applied to the problem of finding optimal feature subsets in the neighborhood rough set reduction process. We define three foraging behaviors of fish to find the optimal subsets and a fitness function to evaluate the best solutions. We construct the neighborhood feature reduction algorithm based on FSA and design some experiments comparing with a heuristic neighborhood feature reduction method. Experimental results show that the FSA-based neighborhood reduction method is suitable to deal with numerical data and more possibility to find an optimal reduct.


Granular computing Rough set theory Neighborhood system Feature reduction Fish swarm algorithm 



This study was funded by Open Fund Project of State International S&T Cooperation Base of Networked Supporting Software (Nos. NSS1404, NSS1405), National Natural Science Foundation of China (No. 61573297), Postdoctoral Science Foundation of China (No. 2014M562306) and Natural Science Foundation of Fujian Province (No. 2015J01277).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyXiamen University of TechnologyXiamenChina
  2. 2.State International S&T Cooperation Base of Networked Supporting SoftwareJiangxi Normal UniversityNanchangChina

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