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

Problem statement Let us assume that there is a training set , S P T 1 2 = , where P is a set of input parameters (features) of an objects and set T is a set of values of the output parameter. Set P is represented as a matrix P p qm QM = ^ h , where p qm is a value of the m -th feature of q -th instance in the set S . Variable m is a feature of the object ( m = 1, 2, ..., M ). Variable q is a number of instance (object) in the sample S ( q = 1, 2, ..., Q ). Value M is measure of cardinality of features of set S ; Q is a cardinality of instances on set S . Set of values of the output parameter is represented as a vector T t q Q = ^ h , where t T q ! l is a value of the output 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.

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