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RETRACTED ARTICLE: Feature selection using fish swarm optimization in big data

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This article was retracted on 01 December 2022

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Abstract

The rapid advances in the field of information and communication technology has made the ubiquitous type of computing along with the internet of things extremely popular. Such applications have created the volumes of the data that are available for the analysis as well as the classification which is an aid to the process of decision making. Among the several methods that are used for the purpose of dealing with the big data, feature selection is found to be very effective. One of the common approaches that involve the searching using a subset of features that have been relevant to that of the topic or will represent an accurate description of this dataset. But unfortunately, the searching using this type of a subset is a problem that is combinatorial and may also be quite time consuming. The meta-heuristic algorithms have been commonly used for the purpose of facilitating the choice of features. Artificial fish swarm optimization (AFSO) algorithms will employ the fish swarming behavior to be the means of overcoming the combinatorial problems. The AFSA has now proved to be highly successful in the applications of a diverse nature. The results of the experiment show that this method proposed will achieve better performance than that of the other methods.

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Authors and Affiliations

  1. Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamilnadu, India

    R. P. S. Manikandan

  2. Department of Computer Science and Engineering, Government College of Engineering, Salem, Tamilnadu, 636011, India

    A. M. Kalpana

Authors
  1. R. P. S. Manikandan
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  2. A. M. Kalpana
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Correspondence to R. P. S. Manikandan.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10586-022-03889-5

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Manikandan, R.P.S., Kalpana, A.M. RETRACTED ARTICLE: Feature selection using fish swarm optimization in big data. Cluster Comput 22 (Suppl 5), 10825–10837 (2019). https://doi.org/10.1007/s10586-017-1182-z

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