Open AccessThe accuracy of Random Forest performance can be improved by conducting a feature selection with a balancing strategy
Author(s) -
Maria Irmina Prasetiyowati,
Nur Ulfa Maulidevi,
Kridanto Surendro
Publication year - 2022
Publication title -
peerj. computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.806
H-Index - 24
ISSN - 2376-5992
DOI - 10.7717/peerj-cs.1041
Subject(s) - oversampling , random forest , feature selection , computer science , feature (linguistics) , fast fourier transform , information gain ratio , information gain , selection (genetic algorithm) , pattern recognition (psychology) , artificial intelligence , data mining , process (computing) , mutual information , tree (set theory) , machine learning , algorithm , mathematics , computer network , linguistics , philosophy , bandwidth (computing) , operating system , mathematical analysis