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- W2913968051 abstract "Two major journals in mechanical engineering field put forward a special note for author(s) of articles discussing structural health monitoring. Only contributing new classifiers or new features without applying engineering knowledge or principle is considered insufficient. This article intends to provide empirical evidence to support the decision. This work shows the classification accuracy of a machine learning approach strongly depends on the number of training data. A high accuracy can simply be obtained by training the classifier with a large dataset, which is hardly realizable in practice. Meanwhile, the features and classifier that based on a sound engineering principle can provide a level of classification accuracy independent of the number of training data." @default.
- W2913968051 created "2019-02-21" @default.
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- W2913968051 date "2018-09-01" @default.
- W2913968051 modified "2023-09-23" @default.
- W2913968051 title "One More Reason to Reject Manuscript about Machine Learning for Structural Health Monitoring" @default.
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- W2913968051 doi "https://doi.org/10.1109/inapr.2018.8627020" @default.
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