The support vector machine method for multiple attribute decision making

In order to capture and represent the decision maker's preferences and then to select the most desirable alternative, a method for solving multiple attribute decision making (MADM) problems is proposed based on support vector machine (SVM). Firstly, the principle of MADM based on SVM is discussed. Secondly, to extract learning samples from the MADM problem, an approach to estimate the utility functions for attributes is presented. Finally, an example is used to illustrate the proposed method.