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A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets.
- Source :
-
Neural Computing & Applications . Oct2019, Vol. 31 Issue 10, p5965-5974. 10p. - Publication Year :
- 2019
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09410643
- Volume :
- 31
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- Neural Computing & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 139232453
- Full Text :
- https://doi.org/10.1007/s00521-018-3414-4