Incorporating Game Theory with Soft Sets

Solving uncertainty issues is a challenge for decision-making. Soft set theory aims to aid complex decision-making when multiple uncertainty variables are involved. We introduce a game-theoretic soft set model to handle the fusion of uncertain data from different information resources and resolve conflicts among parameters in a soft set. The model is utilized to solve three-way classification problems by establishing measurement thresholds for parameters. The experiment shows that the model can strike a balance among different parameters, resulting in a decrease in misclassification error in classification. Furthermore, the extent of the decrease can be fine-tuned by adjusting the ratio between the cost for misclassification error and the cost for undecided error.