🤖 AI Summary
Existing ensemble methods typically rely on a single evaluation criterion for weight assignment, failing to fully exploit multidimensional prior performance information of base classifiers—thus limiting overall model performance. This paper proposes a cooperative-game-theoretic multi-criteria weighted ensemble learning framework. It is the first to introduce the Shapley value into multi-criteria ensemble learning, quantifying each classifier’s marginal contribution across multiple dimensions—including accuracy, stability, and class-wise sensitivity—and integrating these via multi-criteria decision analysis for dynamic weight allocation. The approach ensures both comprehensiveness and fairness in decision-making. Evaluated on the OpenML-CC18 benchmark, it significantly outperforms mainstream weighted ensemble methods, achieving improvements in classification accuracy, robustness, and generalization ability, while effectively mitigating class imbalance and overfitting.
📝 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.