Diagnostic Rule Mining Based on Artificial Immune System for a Case of Uneven Distribution of Classes in Sample

The problem of development automation of classification rules synthesis on the basis of negative selection in the case of uneven distribution of classes in the sample is solved. The method for the synthesis of classification rules on the basis of negative selection in the case of uneven distribution of class instances of sample is proposed. This method uses a priori information about instances of all classes of the sample. The software implementing the proposed method is developed. Some experiments on the solution of practical problem of gas turbine air-engine blade diagnosis are conducted.