A method for solving a classification problem when there is only partial information about some features is proposed. This partial\r\ninformation comprises the mean values of features for every class and the bounds of the features. In order to maximally exploit the\r\navailable information, a set of probability distributions is constructed such that two distributions are selected from the set which\r\ndefine the minimax and minimin strategies. Random values of features are generated in accordance with the selected distributions\r\nby using the Monte Carlo technique. As a result, the classification problem is reduced to the standard model which is solved by\r\nmeans of the support vector machine. Numerical examples illustrate the proposed method.
Loading....