Suppose that you have a dataset consisting of 5 features. The mutual information between each feature and class and also pair-wise mutual information scores among the features are given in Table 1 and Table 2, respectively. Table 1. Mutual information between each feature and class label Feature 1 Feature 2 Feature 3 Feature 4 Feature 5 0.6 0.5 0.8 0.3 0.9 Feature 1 Feature 2 Feature 3 Feature 4 Feature 5 Table 2. Pair-wise mutual information between features Feature 1 Feature 2 Feature 3 Feature 4 0.3 0.2 0.4 0.1 0.4 0.3 Feature 5 0.2 0.15 0.2 0.1 - Definition: According to minimum Redundancy-Maximum Relevance (MRMR) approach, as we have seen in our lectures mth feature chosen for inclusion in the set of selected variables, S, must satisfy the below condition: mRMR algorithm: 1 max I(x;;T) xjeX-Sm-1 m-1 x;Sm-1 where X is the whole set of features; T is the target (class) variable; x; is the ith feature, and I is the mutual information. Rank the features using minimum Redundancy-Maximum Relevance (MRMR) feature ranking algorithm. Calculate and show the mRMR scores of each feature in each step and clearly indicate the feature selected in each step. Suppose that you have a dataset consisting of 5 features. The mutual information between each feature and class and also pair-wise mutual information scores among the features are given in Table 1 and Table 2, respectively. Table 1. Mutual information between each feature and class label Feature 1 Feature 2 Feature 3 Feature 4 Feature 5 0.6 0.5 0.8 0.3 0.9 Feature 1 Feature 2 Feature 3 Feature 4 Feature 5 Table 2. Pair-wise mutual information between features Feature 1 Feature 2 Feature 3 Feature 4 0.3 0.2 0.4 0.1 0.4 0.3 Feature 5 0.2 0.15 0.2 0.1 - Definition: According to minimum Redundancy-Maximum Relevance (MRMR) approach, as we have seen in our lectures mth feature chosen for inclusion in the set of selected variables, S, must satisfy the below condition: mRMR algorithm: 1 max I(x;;T) xjeX-Sm-1 m-1 x;Sm-1 where X is the whole set of features; T is the target (class) variable; x; is the ith feature, and I is the mutual information. Rank the features using minimum Redundancy-Maximum Relevance (MRMR) feature ranking algorithm. Calculate and show the mRMR scores of each feature in each step and clearly indicate the feature selected in each step