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Published in:
2009 | OriginalPaper | Chapter
Feature Selection from Barkhausen Noise Data Using Genetic Algorithms with Cross-Validation
Authors : Aki Sorsa, Kauko Leiviskä
Published in: Adaptive and Natural Computing Algorithms
Publisher: Springer Berlin Heidelberg
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Barkhausen noise is used in non-destructive testing of ferromagnetic materials. It has been shown to be sensitive to material properties but the reported results are more or less qualitative. The quantitative prediction of the material properties from the Barkhausen noise signal is challenging. In order to develop reliable models, the feature selection is critical. The feature selection method applied in this study utilizes genetic algorithms with cross-validation based objective function. Cross-validation is used because the amount of data is limited. The results show that genetic algorithms can be successfully applied to feature selection. The obtained results are reliable and rather consistent with the results obtained earlier.