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

• Published in: Neural computing & applications Vol. 31; no. 10; pp. 5965 - 5974
• Main Authors: Ibrahim, Hadeel Tariq, Mazher, Wamidh Jalil, Ucan, Osman N., Bayat, Oguz
• Format: Journal Article
• Language: English
• Published: London Springer London 01-10-2019; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Biomedical data; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Image Processing and Computer Vision; Optimization; Optimization algorithms; Original Article; Probability and Statistics in Computer Science; Support vector machines; Support vector machines; Feature selection; Grasshopper optimizer
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3414-4

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.