Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data

Authors : Hüseyin Gündoğdu, Osman Altay

Pages : 58-66

Doi:10.53070/bbd.1351629

View : 46 | Download : 101

Publication Date : 2023-10-18

Article Type : Research Paper

Abstract :In knowledge discovery, the processes of applying data cleaning, data integration, data selection-transformation, and data mining methods and obtaining meaningful information from the obtained patterns are performed, respectively. In recent years, it has become quite common to use metaheuristic optimization methods in the data selection phase. In this study, the nearest neighbor algorithm, support vector machine, and decision tree algorithms from machine learning algorithms were used on health data obtained from the University of California, Irvine. The whale optimization algorithm, salp swarm optimization, slime mould optimization, particle swarm optimization, and Harris Hawks optimization methods were used for feature selection. The obtained results were compared in detail.
Keywords : Özellik Seçimi, Meta Sezgisel Optimizasyon Algoritmaları, Baline Optimizasyonu, Harris Şahini Optimizasyonu