Feature selection to increase the random forest method performance on high dimensional data

Feature selection to increase the random forest method performance on high dimensional data

Random Forest is a supervised classification method based on bagging (Bootstrap aggregating) Breiman and random selection of features. The choice of features randomly assigned to the Random Forest makes it possible that the selected feature is not necessarily informative. So it is necessary to selec...

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Bibliographic Details
Main Authors: Maria Irmina Prasetiyowati, Nur Ulfa Maulidevi, Kridanto Surendro
Format: Article
Language:English
Published: Universitas Ahmad Dahlan 2020-11-01
Series:IJAIN (International Journal of Advances in Intelligent Informatics)
Subjects:
random forest
feature selection
bestfirst method
high dimensional data
cnae-9 dataset
Online Access:http://ijain.org/index.php/IJAIN/article/view/471

Internet

http://ijain.org/index.php/IJAIN/article/view/471

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