Tıp Verilerinde Meta-Sezgisel Optimizasyon Yöntemlerinin Özellik Seçimi Performanslarının Karşılaştırılması

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.