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Tuning parameter estimation in SCAD-support vector machine using firefly algorithm with application in gene selection

Niam Abdulmunim Al-Thanoon1, Omar Saber Qasim2, Zakariya Yahya Algamal3

  • 1Department of Operations Research and Artificial Intelligence, University of Mosul, Mosul, Iraq.

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Summary
This summary is machine-generated.

This study introduces a firefly algorithm to optimize tuning parameters for penalized support vector machines (PSVM) with SCAD penalty in cancer classification. The method efficiently identifies key genes for improved diagnostic accuracy.

Keywords:
Cancer classificationFirefly algorithmGene selectionPenalized support vector machineSCAD

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Oncology

Background:

  • Gene selection is crucial for accurate cancer classification.
  • Penalized Support Vector Machines (PSVM) with SCAD penalty are effective but require optimal tuning parameters.

Purpose of the Study:

  • To propose a novel firefly algorithm for determining the optimal tuning parameter in PSVM with SCAD penalty.
  • To enhance gene selection for improved cancer classification performance.

Main Methods:

  • A firefly algorithm, a metaheuristic continuous optimization technique, was employed.
  • The algorithm was used to find the optimal tuning parameter for PSVM with SCAD penalty.
  • The approach was validated on four benchmark gene expression datasets.

Main Results:

  • The proposed firefly algorithm efficiently identified relevant genes.
  • The method achieved superior classification accuracy compared to existing approaches.
  • The algorithm effectively reduced the number of selected genes while maintaining high performance.

Conclusions:

  • The firefly algorithm offers an efficient and effective solution for tuning PSVM with SCAD penalty in gene selection.
  • This approach significantly improves cancer classification accuracy and gene relevance identification.