A Cooperative Game-Based Multi-Criteria Weighted Ensemble Approach for Multi-Class Classification

TL;DR

This paper introduces a novel multi-criteria weighted ensemble approach based on cooperative game theory for multi-class classification, effectively integrating multiple classifier evaluations to improve performance.

Contribution

It proposes a cooperative game-based method to incorporate multiple criteria into ensemble weighting, enhancing model performance over existing single-criterion approaches.

Findings

Superior performance on Open-ML-CC18 dataset

Effective integration of multiple evaluation criteria

Improved ensemble weighting accuracy

Abstract

Since the Fourth Industrial Revolution, AI technology has been widely used in many fields, but there are several limitations that need to be overcome, including overfitting/underfitting, class imbalance, and the limitations of representation (hypothesis space) due to the characteristics of different models. As a method to overcome these problems, ensemble, commonly known as model combining, is being extensively used in the field of machine learning. Among ensemble learning methods, voting ensembles have been studied with various weighting methods, showing performance improvements. However, the existing methods that reflect the pre-information of classifiers in weights consider only one evaluation criterion, which limits the reflection of various information that should be considered in a model realistically. Therefore, this paper proposes a method of making decisions considering various information through cooperative games in multi-criteria situations. Using this method, various types of information known beforehand in classifiers can be simultaneously considered and reflected, leading to appropriate weight distribution and performance improvement. The machine learning algorithms were applied to the Open-ML-CC18 dataset and compared with existing ensemble weighting methods. The experimental results showed superior performance compared to other weighting methods.