In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MADM problem, and by solving this problem with a method from the MADM techniques, we obtain a fuzzy classification. For this purpose, we utilize the technique for order of preference by similarity to ideal solution (TOPSIS) method as a well-known method in the MADM techniques. Additionally, we use a new criterion for determining a weight vector for features in this approach. We evaluate the obtained results of the new approach with five well-known algorithms on ten datasets. Also, we compare our new approach with the weightless algorithm and weighed algorithm by the generalized Fisher score in feature selection methods. Finally, to show the superiority of the new approach, we use a statistical comparison with other methods.

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