TY  - JOUR
T1  - A Cooperative Game-Based Multi-Criteria Weighted Ensemble Approach for Multi-Class Classification
AU  - Yoon, Dongseong 
AU  - Kim, Sungwook 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2025
DA  - 2025/1/1
DO  - 10.7840/kics.2025.50.4.561
KW  - MCDM
KW  - Cooperative Game
KW  - compromise
KW  - Ensemble
KW  - Multi-class classification
KW  - Multi-Criteria
KW  - Game theory
KW  - VIKOR method
AB  - 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 demonstrated an average accuracy improvement of 1.02% and a maximum improvement of 3.15%, showing superior performance compared to other weighting methods.