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Classification using both the whole (filtered) data set, and the feature sets selected by the feature selection methods under investigation, was performed with the Weka machine learning suite.
Although 5metC was selected as informative by the feature selection procedures (Methods), the signal density around the regulated events and non-regulated events did not show clear differences (Additional file 1: Figure S2).
Despite its popularity, the diagnosis result is affected severely by the feature selection and the performance of the classifiers.
For each such frame, the features that were chosen by the feature selection procedure (Section 2.6) are computed.
Also, the set of features obtained by the feature selection process will give insights on the stego scheme and its possible weaknesses.
Table 2 presents the morphological and dynamic features retained by the feature selection procedure and that were used to train the individual classifier.
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We select sensor locations by following the feature selection procedure of minimizing (4) with respect to σ=(σ 1,…,σ M ), where σ i ∈{0,1} d for i=1,…,M.
This was achieved by splitting the feature selection data set into a training set and a test set (again it is important to note that all of the data used in this feature selection step is derived from the training set of the overall classification procedure, and therefore is fully independent of the final classification).
By this means, the feature selection performance of random projection should be improved [12].
We start the derivation of sMPAS by first restating the feature selection problem using the spatial segregation notations.
Additionally, we evaluated several state-of-the-art prediction methods by independently pairing the feature selection and classification components of the predictor.
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