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

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...

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Publicado en:Medical & Biological Engineering & Computing Vol. 57; no. 12; pp. 2673 - 2683
Autores principales: Chatra, Kaveri, Kuppili, Venkatanareshbabu, Edla, Damodar Reddy, Verma, Ajeet Kumar
Formato: Journal Article
Publicado: Springer Nature Dec2019
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Cancer data classification using binary bat optimization and extreme learning machine with a novel fitness function.
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          Chatra, Kaveri
          Kuppili, Venkatanareshbabu
          Edla, Damodar Reddy
          Verma, Ajeet Kumar
        affil: Department of Computer Science and EngineeringNational Institute of Technology Goa, Ponda, India
      sug:
        subj:
          Neoplasms
          Gene Expression
          Algorithms
      ab: 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.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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