A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets
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Tarih
2019
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer London Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Al-Rayes, Hadeel/0000-0001-9749-4024; mazher, wamidh jalil/0000-0003-2092-3745
Anahtar Kelimeler
Support Vector Machines, Feature Selection, Grasshopper Optimizer
Kaynak
Neural Computing & Applications
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
31
Sayı
10
