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Like traditional fusion methods, the features used in the proposed hierarchical fusion method should be complementary to some extent, and the performance of the proposed method will be affected by the setting of the parameter.
As described in Methods, the features for each classifier are ranked using an expanded variation of the basic SVM-RFE (Support Vector Machine – Recursive Feature Elimination) algorithm [ 39].
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In these methods, the feature space is partitioned into subspaces and different probability density functions (pdf) are learned for the different streams.
In these methods, the feature space is partitioned into subspaces generated by the different streams, and different probability density functions (pdf) are learned for each subspace.
To facilitate comparison with existing window-sliding methods, the feature-extraction module employs features that are often used by such methods: the integral features introduced in [ 78].
For almost all methods the feature distances get significantly higher the farther away the patches are located from the CoD (i.e. the more distorted the patches are).
Since there is no robust screening mechanism available to distinguish the two types of features in most subspace feature selection methods, the global features may demonstrate 'obvious' advantages over the local features in the feature selection.
In the first method, the features of the code error-signature are modeled to differentiate between the fault types and fault locations.
In the KNN method, the features are first compared in order to determine the similarity of between a new case and previous cases.
In our method, the features of the graph kernel-based method are extracted by the Stanford Parser [ 17].
Using this piecewise method, the features that are being extracted are statistics of each partition of the time-series.
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