Resolving Conflicts Among Multiple Classifiers Using Game-Theoretic Soft Sets

Addressing uncertainty is a challenge in decisionmaking. Soft set theory aids in complex scenarios involving multiple opinions. We propose a game-theoretic soft set model to resolve conflicts in soft sets, particularly when merging multiple classifiers. This model allows each classifier to make strategic selections from objects associated with conflicting opinions. The experimental results demonstrated that the model effectively balances the decisions of all classifiers while maintaining satisfactory classification performance.