Diagnostic Rule Mining Based on Artificial Immune System for a Case of Uneven Distribution of Classes in Sample
Diagnostic Rule Mining Based on Artificial Immune System for a Case of Uneven Distribution of Classes in Sample
Author(s): Sergey Subbotin, Andrii Oliinyk, Vitaly Levashenko, Elena ZaitsevaSubject(s): Methodology and research technology, ICT Information and Communications Technologies
Published by: Žilinská univerzita v Žilině
Keywords: artificial immune system; instance; negative selection; classification; recognition error; sample;
Summary/Abstract: 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.
Journal: Komunikácie - vedecké listy Žilinskej univerzity v Žiline
- Issue Year: 18/2016
- Issue No: 3
- Page Range: 3-11
- Page Count: 9
- Language: English
