Self-adaptive transfer for decision trees based on similarity metric

Abstract

Negative transfer, transfer opportunity and transfer method are the most key problems affecting the learning performance of transfer learning. In order to solve these problems, a self-adaptive transfer for decision trees based on a similarity metric (STDT) is proposed. At first, according to whether the source task datasets to be allowed to access, a prediction probability based on constituents or paths is adaptively used to calculate the affinity coefficient between decision trees, which can quantify the similarity degree of related tasks. Secondly, a judgment condition of multi-sources is used to determine whether the multi-source integrated transfer is adopted. If do, the similarity degrees are normalized, which can be viewed as transfer weights assigned to source decision trees to be transferred. At last, the source decision trees are transferred to assist the target task in making decisions. Simulation results on UCI and text classification datasets illustrate that, compared with multisource transfer algorithms, i.e., weighted sum rule (WSR) and MS-TrAdaBoost, the proposed STDT has a faster transfer speed with the assurance of high decision accuracy. Copyright © 2013 Acta Automatica Sinica. All rights reserved.