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As discussed in the previous section, classical validation methods cannot be used to assess the transitive relationship method results.
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Classical model validation methods (e.g. k-fold cross-validation or Receiving Operator Characteristic (ROC) curves) are suitable to validate probabilistic but not discrete results [ 17], which are represented by a single point in the ROC space.
Unlike the classical cross-validation methods common in evaluation of machine learning algorithms, our experimental protocol accounts for a temporal nature of data in security applications and never predicts the past data from the future one.
Due to interdependencies between these different system elements the complexity of PSS is very high, thus preventing a simple transfer of validation methods of classical product development or service engineering.
Validation methods were based on recently proposed and classical approaches that engage exact mathematical methods.
In both validation methods, a validation value greater than 0.5 indicates a robust and predictive model.
However, classical validation does not support the user in better understanding the properties of the model or the underlying data.
There are two common validation methods— cross-validation and external validation (Tong et al. 2003c).
Both validation methods confirmed our immunohistochemical findings.
However, these validation methods are rarely used.
The accuracy of these methods has been calculated with validation methods such as v-fold validation.
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