1. A hybrid random forest (RF) and discriminant analysis (DA) method of training a computerized system to predict the class membership of a sample of unknown class, comprising: providing a forest training set to the computerized system comprising N feature vector ({circumflex over (x)} i ) and class label (ŷ i ) pairs, ({circumflex over (x)} i ε ,ŷ i ε{0,1}) for i=1 to N, and from D available features; and controlling the computerized system to repeat the following set of steps until a desired forest size having n decision trees has been reached: adding a decision tree to the forest, creating a tree training set associated with the added decision tree, said tree training set comprising N bootstrapped training samples randomly selected with replacement from the forest training set, and using the tree training set to train the added decision tree by using hierarchical DA-based decisions to perform splitting of decision nodes and thereby grow the added decision tree as a DA-based decision tree, whereby, upon reaching the desired forest size, the computerized system may predict the classification of a sample of unknown class using the n DA-based decision trees.