A cluster-preserving feature selection framework cut intrusion detection features from 78 to 27 on the CICIDS2017 benchmark while Random Forest and XGBoost maintained roughly 99.9 percent accuracy.

Feature selection (FS) is a critical step in hyperspectral image (HSI) classification, essential for reducing data dimensionality while preserving classification accuracy. However, FS for HSIs remains ...

A new entropy-weighted feature selection method trims 13 clinical attributes down to 8 and pushes heart disease prediction ...

This article is excerpted from the course "Fundamental Machine Learning," part of the Machine Learning Specialist certification program from Arcitura Education. It is the sixth part of the 13-part ...