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Neural Computing and Applications

, Volume 28, Issue 10, pp 2995–3008 | Cite as

Tolerance rough set firefly-based quick reduct

  • Jothi Ganesan
  • Hannah H. Inbarani
  • Ahmad Taher AzarEmail author
  • Kemal Polat
New Trends in data pre-processing methods for signal and image classification

Abstract

In medical information system, there are a lot of features and the relationship among elements is solid. In this way, feature selection of medical datasets gets awesome worry as of late. In this article, tolerance rough set firefly-based quick reduct, is developed and connected to issue of differential finding of diseases. The hybrid intelligent framework intends to exploit the advantages of the fundamental models and, in the meantime, direct their restrictions. Feature selection is procedure for distinguishing ideal feature subset of the original features. A definitive point of feature selection is to build the precision, computational proficiency and adaptability of expectation strategy in machine learning, design acknowledgment and information mining applications. Along these lines, the learning framework gets a brief structure without lessening the prescient precision by utilizing just the chose remarkable features. In this research, a hybridization of two procedures, tolerance rough set and as of late created meta-heuristic enhancement calculation, the firefly algorithm is utilized to choose the conspicuous features of medicinal information to have the capacity to characterize and analyze real sicknesses. The exploratory results exhibited that the proficiency of the proposed system outflanks the current supervised feature selection techniques.

Keywords

Rough set theory Tolerance rough set Firefly algorithm Soft computing techniques Swarm intelligent Supervised feature selection 

Notes

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Jothi Ganesan
    • 1
  • Hannah H. Inbarani
    • 2
  • Ahmad Taher Azar
    • 3
    Email author
  • Kemal Polat
    • 4
  1. 1.Department of Information TechnologySona College of Technology (Autonomous)SalemIndia
  2. 2.Department of Computer SciencePeriyar UniversitySalemIndia
  3. 3.Faculty of Computers and Information, Benha UniversityBenhaEgypt
  4. 4.Department of Electrical and Electronics Engineering, Faculty of Engineering and ArchitectureAbant Izzet Baysal UniversityBoluTurkey

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