You are in:Home/Publications/Jothi G, Inbarani HH, Azar AT, Polat K (2016) Tolerance Rough Set Firefly based Quick Reduct. Neural Computing and Applications, Springer. DOI: 10.1007/s00521-016-2514-2. [ISI Indexed: Impact Factor: 1.5].

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Jothi G, Inbarani HH, Azar AT, Polat K (2016) Tolerance Rough Set Firefly based Quick Reduct. Neural Computing and Applications, Springer. DOI: 10.1007/s00521-016-2514-2. [ISI Indexed: Impact Factor: 1.5].
Authors: Not Available
Year: 2016
Keywords: Rough set theory Tolerance rough set Firefly algorithm Soft computing techniques Swarm intelligent Supervised feature selection
Journal: Neural Computing and Applications
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Springer
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available
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.

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