Table 2.

Mutual information-based feature selection Algorithms.

Algorithms	Description
MIFS [20]	A greedy approach that selects only highly informative feature and forms an optimal feature subset. It identifies the non-linear relationship between the selected feature and its output class to reduce the amount of redundancy and uncertainty in the feature vector [61,62,63].
mRMR [21,22]	An incremental feature selection algorithm that forms an optimal feature subset by selecting features with minimum Redundancy and Maximum Relevancy.
CIFE [23]	It forms an optimal feature subset that maximizes the class-relevant information between the features by reducing the redundancies among the features.
JMI [24]	An increment to mutual information which finds the conditional mutual information to define the joint mutual information among the features and eliminates the redundant features if any.
CMI [25]	Selects the features only if it carries additional information and eases the prediction task of the output class.
DISR [26]	DISR measures the symmetrical relevance and combines all features variable to describe more information about the output class instead of focusing on individual feature information.
ICAP [27]	Features are selected based on the interactions and understand the regularities of the feature set.
CONDRED [28]	Identifies the conditional redundancy exists between the features.