The accuracy of Random Forest performance can be improved by conducting a feature selection with a balancing strategy.

ABSTRACT: One of the significant purposes of building a model is to increase its accuracy within a shorter timeframe through the feature selection process. It is carried out by determining the importance of available features in a dataset using Information Gain (IG). The process is used to calculate the amounts of information contained in features with high values selected to accelerate the performance of an algorithm. In selecting informative features, a threshold value (cut-off) is used by the Information Gain (IG). Therefore, this research aims to determine the time and accuracy-performance needed to improve feature selection by integrating IG, the Fast Fourier Transform (FFT), and Synthetic Minor Oversampling Technique (SMOTE) methods. The feature selection model is then applied to the Random Fo

SUBMITTER: Prasetiyowati MI 

PROVIDER: S-EPMC9299283 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature