In radiomics, feature selection methods are primarily used to eliminate redundant features and identify relevant ones. Feature projection methods, such as principal component analysis (PCA), are often ...

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 ...

It is a significant and challenging task to detect the informative features to carry out explainable analysis and build an interpretable AI system for high dimensional data, especially for those with ...