Comparison of hybrid binary GWO-PSO algorithm with feature selection methods by using machine learning classifiers

Authors : Buğra Kaan Tiryaki

Pages : 170-187

Doi:10.25092/baunfbed.1469682

View : 35 | Download : 52

Publication Date : 2025-01-20

Article Type : Research Paper

Abstract :In the field of machine learning, feature selection methods used in the pre-processing of data for the classifier have become very popular. Instead of the whole dataset, it is important to create a new sub-dataset by discarding the irrelevant and redundant variables in the dataset to make the data ready for analysis. In this way, both the performance of the learning classifier will increase, and cost and time savings will be achieved. In this study, the performance of the hybrid binary grey wolf optimization - particle swarm optimization (BHGWOPSO) algorithm with machine learning methods is investigated. In addition, a comparison was made between BHGWOPSO and other feature selection methods such as principial component analysis and filter methods in contrast to literature. Thus, it is aimed to show which of the different feature selection methods will work better. For this purpose, five different benchmark datasets with different number of features were selected. Both feature selection methods and machine learning classifiers were compared with each other using the accuracy metric. As a result of the comparisons, it was observed that a different feature selection method and a different classifier had higher accuracy values for each data set.
Keywords : İkili hibrit optimizasyon, Özellik seçimi, Öğrenme sınıflandırıcıları, Sarmal yöntem.