Wear particle classification using genetic programming evolved features
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- @Article{Xu:2018:LubricationScience,
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author = "Bin Xu and Guangrui Wen and Zhifen Zhang and
Feng Chen",
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title = "Wear particle classification using genetic programming
evolved features",
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journal = "Lubrication Science",
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year = "2018",
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volume = "30",
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number = "5",
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pages = "229--246",
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month = aug,
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1557-6833",
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DOI = "doi:10.1002/ls.1411",
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abstract = "This paper explores the feasibility of applying
genetic programming (GP) to classify wear particles. A
marking threshold filter is proposed to preprocess
ferrographic images before optimising the feature space
of wear particles using GP. Subsequently, evolved
features by GP are quantitatively evaluated by the
Fisher criterion and distance fitness function, and
clustering performance is evaluated qualitatively. The
evolved features are compared with a conventional
feature set as the inputs to support vector machines,
probabilistic neural networks, and k-nearest neighbour.
Results demonstrated that the evolved features
indicated a significant improvement in classification
accuracy and robustness compared with conventional
features. Finally, 3 typical wear particles, sliding,
cutting, and oxidative, are successfully classified.",
- }
Genetic Programming entries for
Bin Xu
Guangrui Wen
Zhifen Zhang
Feng Chen
Citations