2016
An urn model for majority voting in classification ensembles
NeurIPS 2016poster
In this work we analyze the class prediction of parallel randomized ensembles by majority voting as an urn model. For a given test instance, the ensemble can be viewed as an urn of marbles of different colors. A marble represents an individual classifier. Its color represents the class label predict…