PRODUCTION RULES EXTRACTION BASED ON NEGATIVE SELECTION

Authors

  • A. A. Oliinyk Zaporizhzhya National Technical University, Zaporizhzhya, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2016-1-5

Keywords:

sample, diagnostics, model of quality control, negative selection, production rule.

Abstract

The problem of mathematical support development is solved to automate the extraction knowledge as production rules from the training
data samples. The object of study is the process of constructing models of non-destructive quality control. The subject of study are methods
of production rules extraction based on negative selection for synthesis of quality control models. The purpose of the work is to develop a
method of production rules synthesis on the basis of a set of detectors is in the handling of data of training sample, characterized by a substantial number of instances of distinction belonging to different classes. A method for the synthesis of production rules on the basis of negative selection in the case of uneven distribution of instances of the sample classes is proposed. The developed method allows to exclude irrelevant and redundant features from the sample, thereby reducing the search space and time of execution of the method, as well as generate a set of detectors with high approximation and generalization capability. The proposed method improves the generalizing properties of synthesized model and its interpretability. The software implementing proposed method is developed. The experiments to study the properties of the proposed method are conducted. The experimental results allow to recommend the proposed method for use in practice.

References

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Published

2015-01-26

How to Cite

Oliinyk, A. A. (2015). PRODUCTION RULES EXTRACTION BASED ON NEGATIVE SELECTION. Radio Electronics, Computer Science, Control, (1). https://doi.org/10.15588/1607-3274-2016-1-5

Issue

No. 1 (2016): Radio Electronics, Computer Science, Control

Section

Neuroinformatics and intelligent systems

License

Copyright (c) 2016 A. A. Oliinyk

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