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Medical & Biological Engineering & Computing

, Volume 57, Issue 12, pp 2673–2682 | Cite as

Cancer data classification using binary bat optimization and extreme learning machine with a novel fitness function

  • Kaveri Chatra
  • Venkatanareshbabu KuppiliEmail author
  • Damodar Reddy Edla
  • Ajeet Kumar Verma
Original Article
  • 92 Downloads

Abstract

Cancer classification is one of the crucial tasks in medical field. The gene expression of cells helps in identifying the cancer. The high dimensionality of gene expression data hinders the classification performance of any machine learning models. Therefore, we propose, in this paper a methodology to classify cancer using gene expression data. We employ a bio-inspired algorithm called binary bat algorithm for feature selection and extreme learning machine for classification purpose. We also propose a novel fitness function for optimizing the feature selection process by binary bat algorithm. Our proposed methodology has been compared with original fitness function that has been found in the literature. The experiments conducted show that the former outperforms the latter.

Graphical Abstract

Classification using Binary Bat Optimization and Extreme Learning Machine

Keywords

Gene Cancer DNA 

Notes

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Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology GoaPondaIndia

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