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For balanced data sets, the numbers of training samples in each class are identical.
This study is important since it was the first to try to address the class imbalance inherent to real world spam detection, in contrast to previous studies that constructed balanced, or roughly balanced data sets.
The repeated-measures ANOVA is an appropriate approach, but has a limited applicability, because it only works for balanced data sets.
Change over the follow-up period will be assessed using generalised estimating equations (GEE), which will allow for the best use of all data collected as GEEs do not require balanced data sets.
The accuracy is only applicable for balanced data sets like the Kazius and CA data set.
However, they are normally strictly controlled and often have complete and balanced data sets collected by a dedicated group of individuals working to a standard clinical efficacy protocol [20], [33].
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The data sets are processed with over_sampling techniques to generate balanced data set for required analysis.
Data are chosen based on producing an azimuthally balanced data set, while avoiding the inclusion of data with small signal-to-noise ratios.
The latter criterion was chosen in order to guarantee performance balanced data set.
For subjects with five working days, four days were randomly selected in order to obtain a balanced data set for further processing.
Presumably, the most likely deviations to occur appear in the form of slight departures from a completely balanced data set; for instance that some measurement occasions fail for some subjects but are compensated by more occasions from others.
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