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Their experimentation indicates that the AIS-based classification paradigm has the intrinsic property of dealing more efficiently with highly skewed datasets.
Skewed datasets are known to complicate both learning and performance evaluation [ 52].
A variation of F1, namely, Macro-F1, is normally reported to evaluate classification effectiveness on skewed datasets.
Called the Inclusive Images Competition, the data challenge focuses on how new algorithmic techniques can be used to successfully train deep learning computer vision models on highly geographically skewed datasets, yet not inherit their biases.
The Kruskal-Wallis test followed by Nemenyi Multiple Comparison test was used for skewed datasets.
Data in the tables and figures are presented as means ± SEM or median (semi-interquartile range: [75th 25th percentile]/2) for skewed datasets.
Similar(53)
On the technical side, it has to be borne in mind that our knowledge of protein interactions is certainly incomplete and may contain many false positive interactions [ 55, 56] and the employed technologies may skew the datasets towards particular interactions [ 57].
The distributions are positively and negatively skewed for both datasets.
In this study the "reasonable proxy" relationship is evaluated across a highly skewed daily precipitation dataset in Minnesota for H1,H2 and H3.
To create the simulation data, we first applied iASeq to the real GM12878 data to identify 86,353 SNPs that were not skewed in any dataset using Pr(a i =0| X i, N i, Π, V, W )>0.5 as cutoff.
Although this strategy has attracted significant research attention, most studies neglect an important problem, namely, that most DNA microarray datasets are skewed, which causes traditional learning algorithms to produce inaccurate results.
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