Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm

Abstract

Feature selection algorithms are of great importance in the field of machine learning. Significant reduction of very large data is the main function of feature selection algorithms. These methods are still being developed today. The reason for this is that data structures are growing day by day. As the data increases, more advanced, better performance, feature selection algorithms are needed. In this study, Eta Correlation Coefficient based E-Score Feature selection algorithm was developed. Two versions were prepared for E-Score. We tested the performance of the E-Score method with three classifiers and compared with conventional F-Score Feature Selection Algorithm. According to the results, both versions of the E-Score feature selection algorithm have improved performance and is better than the F-Score. According to these results, it is thought that the E-Score Feature Selection Algorithm can be used in the field of machine learning.