A New Method for Constructing Ensemble Classifier in Privacy-Preserving Distributed Environment

摘要

How to build a classifier on datasets that are distributed across different sites under privacy constrains has attracted much attention during the past few years. In this paper, a new method for constructing classifier ensemble for privacy-preserving distributed data mining is proposed. Different from existing methods, the proposed method can obtain, without any auxiliary assumption and releasing the original data of stakeholders, the optimal weights for classifier combination. Experiments show that the ensemble based on our approach achieved high performance.