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Neural Computing and Applications
Published in: Neural Computing and Applications 10/2019

10-03-2018 | Original Article

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

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

Published in: Neural Computing and Applications | Issue 10/2019

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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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Appendix
Available only for authorised users
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Metadata
Title
A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets
Authors
Hadeel Tariq Ibrahim
Wamidh Jalil Mazher
Osman N. Ucan
Oguz Bayat
Publication date
10-03-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3414-4

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