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Results from phishGILLNET2 shows boosting significantly improves classification performance.
This not only improves classification accuracy but also reduces the time complexity of classification.
Having few regions improves classification performance; however, this may result in a low recognition accuracy.
We demonstrate that the feature selection step improves classification accuracy significantly over a baseline classifier.
It is well known that an ensemble of several classifiers improves classification accuracy.
We demonstrate that this system improves classification accuracy significantly over a single-classifier system.
Similar(11)
Improved classification accuracy.
Adding EHR phenotypes improved classification.
Feature combinations can improve classification results [9].
The technique delivers improved classification with comprehensive interpolated minority instances.
Therefore, we can effectively improve classification efficiency using feature selection.
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