Attribute reduction is important in rough set theory. This study introduces a new method that combines multiple criteria to define attribute reducts, which are minimal subsets of attributes that meet specific requirements. By using a three-way decision-theoretic rough set model, the researchers developed a multi-objective attribute reduction approach that considers positive region, decision cost, and mutual information. The results show that this new method improves classification performance on various datasets.