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Table 6 Subdivisions of the skin sensitisation data set into binary classes.
First, they used the One-vs.-All (OVA) binarization technique [61] for decomposing original dataset into subsets of binary classes.
With variations in size, shape, and background structure, a total of 600 microscopic images are analyzed for the binary classes of healthy and sick conditions of OPMD.
The smoothed posterior score fusion of subsystems shows the best classification performance (73.5% for unweighted, and 72.8% for weighted, average recalls of the binary classes).
As conventional SVM support only binary classes, a multi-class problem is solved by using a common one-against-all (OAA) approach.
A Perceptron classifier [34], [35] uses a hyperplane to separate examples from a dataset D onto different half-spaces corresponding to binary classes.
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The classifier was trained on a balanced binary class distribution.
This activity spectrum can be modeled using (ensembles of) binary class estimators, for instance by combining multiple binary class RF models (Fig. 1).
The clipped images were classified into a binary class: urban built-up and others.
With binary class QSAR, individual models are constructed for every target.
These are standard datasets used in literature which have varying levels of binary class imbalance.
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