Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms

Algorithm 1

Random forest algorithm.

  1. For b =1 to B:

    a. Draw a bootstrap sample of size N from the training data.

    b. Grow a random-forest tree T_b to the bootstrapped data, by recursively repeating the following steps for each terminal node of the tree, until the minimum node size n_min is reached.

      i. Select m variables at random from the p variables.

      ii. Pick the best variable/split-point among the m.

      iii. Split the node into two daughter nodes.

  2. Output the ensemble of trees

To make a prediction at a new point x:

Classification (voting): Let be the class prediction of the b^th random-forest tree.