Unsupervised correlation-based feature reduction
Two correlation-based feature reduction (CFR) methods were evaluated. They both work by iteratively discarding one feature from the most correlated pair of features, but they differ in how they choose which feature to discard during each iteration.Firstly, the standard CFR method computes the mean absolute correlation between both features and the other remaining features, and the feature with the highest mean correlation is removed. An example of this algorithm is implemented by the function A limitation of this approach is that ignoring information on feature interpretability could result in a radiomics model that includes features that are harder to interpret. We therefore propose a hierarchical CFR approach, where a hierarchy of feature groups is pre-specified based on an overall judgment of the interpretability of the features within each group. On comparison of two highly correlated features the feature in the lower-ranking group will be discarded. When two features are in the same feature group, the standard CFR rule is applied to determine which feature to discard. The following hierarchy is used here: 