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The parameters of the HDTLM are calibrated by a design of experiments (DOE) on randomly generated testing instances.
Albeit theoretically suboptimal, the heuristic algorithm performance very close to the optimal solution for most testing instances summarized in the next section.
Note that the minimum agreement is based on the original five degrees per dimension and that we consider this subset only for the testing instances, as we want to keep training conditions fixed for better transparency of effects of prototypization.
ROC1 and ROC2 are obtained from the same testing instances but in different orders.
Finally, in round 3, we partition the list into three parts: the top and middle parts contain 12% and 18% of the total number of testing instances, respectively.
The predictions made for the testing instances are compared with the defined class labels (binding or non-binding) to evaluate the predictor.
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The ensemble classifier predicts a class for a testing instance which is predicted by the majority of base classifiers.
It updates the testing instance label set predictions and the relational features of label and instance correlations.
Again, all test instances were swiftly computable.
Experiments were on two sets of 11 representative test instances.
For the 32 test instances, A-ENS achieves better HV values than accurate non-dominated sorting on 19 test instances in KnEA, and 24 instances in Two_Arch2.
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