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He later extended his summary to the following: Part I: The general state of affairs in the world today as it affects all individuals, minorities, classes or races that are felt to be outside the ruling conventions.
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Over_sampling of the minority classes help to improve the performance.
The remaining minority classes are arranged as per their respective status of lowest minority class having I.R. > 1.5.
On a highly imbalanced class distribution, it is particularly demanding to identify instances from minority classes.
On a highly imbalanced class distribution, it is particularly demanding to identify examples from minority classes.
Considering the minority classes were less than 1%, this is very promising.
A direct consequence is that minority classes cannot be well modeled, and the final performance decays.
Furthermore it rationalizes the overshooting issue of the minority classes after the over_sampling process.
Minority classes may correspond to specific information needs which are relevant for specific groups of users.
So, the wL-GEM is proposed to balance classes by penalizing heavier for generalization error being made in minority classes.
The over_sampling process is repeated for the remaining lowest minority classes belonging to multi-class data set satisfying I.R. > 1.5.
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