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In machinery fault diagnosis area, the obtained data samples under faulty conditions are usually far less than those under normal condition, resulting in unbalanced dataset issue. The commonly used machine learning techniques including Neural Network, Support Vector Machine, and Fuzzy C-Means, etc. are subject to high misclassification with unbalanced datasets. On the other hand, Support Vector Data...
In mechanical fault diagnosis area, fault samples are often difficult to obtain, so the number of fault samples is far less than that of normal samples which leads to the unbalanced dataset issues. A novel model combining SVDD (Support Vector Data Description) and binary tree (BT) based on Mahalanobis distance is put forward to address the multi-classification problems under unbalanced datasets. The...
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