期刊
NEURAL COMPUTING & APPLICATIONS
卷 31, 期 10, 页码 5965-5974出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-018-3414-4
关键词
Support vector machines; Feature selection; Grasshopper optimizer
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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