A New Random Forest Algorithm Based on Learning Automata

Algorithm 1

The random forest pseudocode for classification applications [1].

	Let D = {(x1, y1), (x2, y2), …, (xN, yN)} denote the training data, with xi = (xi,1, xi,2, …, xi,p)^T
	For j = 1 to J:
	Take a bootstrap sample D of size N from D.
	Using the bootstrap sample, Dj as the training data fit a tree.
(a)	Start with all observations in a single node.
(b)	Repeat the following steps recursively for each node until the stopping criterion is met: (i) Select m predictors at random from the p available predictors.
	Find the best binary split among all binary splits in the predictors from step (i).
	Split the node into two descendant nodes using the split from step (ii).
	To make a prediction at a new point x.
	Where is the prediction of the response variable at x using the jth tree.