Modified Multi-Criteria Decision Making Methods to Assess Classification Methods

During the last few decades, many academic and professional groups gave attention to adopting multi-criteria decision-making methods in a variety of contexts for that are given diversity sophistication their selections. Five different classification tested assessed this paper. Each has its own set five attribute selection approaches. By using procedures, these data can be used rate options. Technique order preference by similarity ideal solution (TOPSIS) is designed utilizing modified fuzzy analytic hierarchy process (MFAHP) compute weight alternatives TOPSIS obtain confidence value each classifier feature approach individually. Defuzzification values final criteria weights, rank function used. The modification tests prediction models (alternatives) six performance measurements (criteria) analyze German credit sets. Overall results experiment show proposed strategies successful approval data.