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 is a critical pre-processing step in machine learning that seeks to identify a subset of input variables most relevant to predictive modelling. By reducing dimensionality, it ...

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