A new multi-attribute decision-making method for interval data using support vector machine

There are numerous and various methods for solving the Multi-Attribute Decision-Making (MADM) problems in the literature, such as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Elimination and Choice Expressing Reality (ELECTRE), Analytic Hierarchy Process (AHP), etc. We have explored Support Vector Machine (SVM) as an efficient method for solving MADM problems. The SVM technique was proposed for classifying data at first. At the same time, in the current research, this popular method will be used to sort the preference alternatives in a MADM problem with interval data. The accuracy of the proposed technique will be compared with a popular extended method for interval data, say interval TOPSIS. Numerical experiments showed that admissible results can be obtained by the new method.