A deep forest classifier with weights of class probability distribution subsets

Abstract

Abstract A modification of the Deep Forest or gcForest proposed by Zhou and Feng for solving classification problems is proposed in the paper and called as PM-DF. The main idea for improving classification performance of the Deep Forest is to assign weights to subsets of the class probability distributions at the leaf nodes computed for every training example. The subsets of probability distributions are defined by using Walley’s imprecise pari-mutuel model which compactly divides the unit simplex of probabilities into subsets and allows us to simplify the algorithm of the weight calculation. The weights of the distribution subsets can be viewed in this case as second-order probabilities over subsets of the probability simplex. The optimal weights are computed by solving the standard quadratic optimization problem. The numerical experiments illustrate PM-DF.