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A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets

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Abstract

Support vector machines (SVM) are one of the important techniques used to solve classifications problems efficiently. Setting support vector machine kernel factors affects the classification performance. Feature selection is a powerful technique to solve dimensionality problems. In this paper, we optimized SVM factors and chose features using a Grasshopper Optimization Algorithm (GOA). GOA is a new heuristic optimization algorithm inspired by grasshoppers searching for food. It approved its ability to solve real-world problems with anonymous search space. We applied the proposed GOA + SVM approach on biomedical data sets for Iraqi cancer patients in 2010–2012 and for University of California Irvine data sets.

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Authors and Affiliations

  1. Diyala University, Diyala, Iraq

    Hadeel Tariq Ibrahim

  2. Southern Technical University, Basra, Iraq

    Wamidh Jalil Mazher

  3. Department of Electrical and Electronics Engineering, Altinbas University, Istanbul, Turkey

    Hadeel Tariq Ibrahim, Wamidh Jalil Mazher, Osman N. Ucan & Oguz Bayat

Authors
  1. Hadeel Tariq Ibrahim
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  2. Wamidh Jalil Mazher
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  3. Osman N. Ucan
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  4. Oguz Bayat
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Correspondence to Hadeel Tariq Ibrahim.

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Ibrahim, H.T., Mazher, W.J., Ucan, O.N. et al. A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets. Neural Comput & Applic 31, 5965–5974 (2019). https://doi.org/10.1007/s00521-018-3414-4

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  • DOI: https://doi.org/10.1007/s00521-018-3414-4

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