An Improved Feature Selection Based on Effective Range for Classification

Algorithm 1

Input: Data matrix 𝑋∈𝑅^𝑁×𝑑, 𝑖=1,2,…,𝑑, the number of

selected feature k.

Output: Feature subset.

(1) Compute the ER of each feature by (5);

(2) Compute the 𝑂𝐴_𝑖 and 𝐼𝐴_𝑖 by (6) and (7);

(3) Compute the 𝐴𝐶_𝑖 by (9);

(4) Normalize the 𝐴𝐶_𝑖 by (10);

(5) Calculate the 𝑁𝐻_𝑖 and 𝐺𝐻_𝑖 by (11);

(6) Compute the weight of each feature by (12);

(7) Sort the weight of all features in a descending order;

(8) Select the best k features;