How mRMR handles continuous variables?
A. mRMR is a framework where the relevance & redundancy terms should be combined. Mutual information, which is used most of the time, is a useful method to define these two terms, but other options exist. There are three ways for mRMR to handle continuous variables. (1) Use t-test / F-test (bi-class/multiclass) for relevance measure and the correlation among variables as redundancy measure. Other scores such as distances can also be considered. See the CSB03 & JBCB05 papers for details. (2) Use mutual information of discrete variables, – this needs to first discretize variables/features. We have chosen to discretize them using mean+/-alpha*std (alpha=1 or 0 or 2 or 0.5). The choice of alpha will have some influence on the actual features selected (more correctly, the ordering of features you select, – but if you select several more, you may find a lot of them are the same, although may be in different order). This is actually a very robust way to select features. See the TPAMI05, CSB03
