The problem of induction of classification rules on the basis of negative selection in the case of uneven distribution of classes in the sample is solved. The new method for the induction of such rules is proposed. This method uses a priori information about instances of all classes in the sample. A hypercube of maximum possible volume is used as a form of detector. It allows to exclude irrelevant and redundant features from the sample, thereby reducing the search space and the time of execution of the method, as well as to generate a set of detectors with high approximation and generalization capability. The software implementing the proposed method is developed. Some experiments on the solution of problem of gas turbine air-engine blade diagnosis are conducted.

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