A Deep Forest for Transductive Transfer Learning by Using a Consensus Measure

Abstract

A Transfer Learning Deep Forest (TLDF) 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 whose aim is to implement the transductive transfer learning. The transfer learning is based on introducing weights of trees in forests which impact on the forest class probability distributions. The weights can be regarded as training parameters of the deep forest and are determined in order to maximize the agreement on target and source domains. The convex quadratic optimization problem with linear constraints is obtained to compute optimal weights for every forest taking into account the consensus principle. The numerical experiments illustrate the proposed distance metric method.