An ensemble learning framework based on group decision making

TL;DR

This paper introduces an ensemble learning framework based on group decision making, where base learners are decision-makers, and performance metrics guide their combination, improving classification accuracy in multi-class problems.

Contribution

The paper proposes a novel ensemble learning approach using group decision making principles, incorporating performance metrics and OvR strategy for multi-class classification.

Findings

Higher accuracy than six popular classifiers in most tests

Effective handling of multi-class classification via OvR

Demonstrated superiority through experimental validation

Abstract

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.