  A Discriminative Deep Forest (DisDF) as a metric learning algorithm is
proposed in the paper. It is based on the Deep Forest or gcForest proposed by
Zhou and Feng and can be viewed as a gcForest modification. The case of the
fully supervised learning is studied when the class labels of individual
training examples are known. The main idea underlying the algorithm is to
assign weights to decision trees in random forest in order to reduce distances
between objects from the same class and to increase them between objects from
different classes. The weights are training parameters. A specific objective
function which combines Euclidean and Manhattan distances and simplifies the
optimization problem for training the DisDF is proposed. The numerical
experiments illustrate the proposed distance metric algorithm.
