BaggingLMS: A bagging-based linear fusion with least-mean-square error update for regression

Abstract

The merits of linear decision fusion in multiple learner systems have been widely accepted, and their practical applications are rich in literature. In this paper we present a new linear decision fusion strategy named Bagging.LMS, which takes advantage of the least-mean-square (LMS) algorithm to update the fusion parameters in the Bagging ensemble systems. In the regression experiments on four synthetic and two benchmark data sets, we compared this method with the Bagging-based Simple Average and Adaptive Mixture of Experts ensemble methods. The empirical results show that the Bagging.LMS method may significantly reduce the regression errors versus the other two types of Bagging ensembles, which indicates the superiority of the suggested Bagging.LMS method