  The classification problem is a significant topic in machine learning which
aims to teach machines how to group together data by particular criteria. In
this paper, a framework for the ensemble learning (EL) method based on group
decision making (GDM) has been proposed to resolve this issue. In this
framework, base learners can be considered as decision-makers, different
categories can be seen as alternatives, classification results obtained by
diverse base learners can be considered as performance ratings, and the
precision, recall, and accuracy which can reflect the performances of the
classification methods can be employed to identify the weights of
decision-makers in GDM. Moreover, considering that the precision and recall
defined in binary classification problems can not be used directly in the
multi-classification problem, the One vs Rest (OvR) has been proposed to obtain
the precision and recall of the base learner for each category. The
experimental results demonstrate that the proposed EL method based on GDM has
higher accuracy than other 6 current popular classification methods in most
instances, which verifies the effectiveness of the proposed method.
