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The proposed over-sampling method generates useful synthetic samples for the classification of imbalanced biomedical data.
The experimental results on eight real-world benchmark datasets have shown that the proposed over-sampling method generates useful synthetic samples for the classification of imbalanced biomedical data.
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we can say that our method generated useful synthetic samples for type IV and VIII.
Such investigative methods can generate useful statistics and hypotheses about how a suicidal impulse might start and how it travels from thought to action, but that's not the same as objective evidence about how it unfolds in real time.
Although the approach using ITS2 was successful in identifying pollen (Richardson et al., 2015), it suffered from two limitations: (1) the method failed to detect certain prominent taxa identified microscopically and (2) while the method generated a useful taxonomic list, the relative abundance of different pollen types could not be inferred from the sequence data.
Briefly saying, our over-sampling method enables to generate useful synthetic samples by referring to actual samples taken from real-world datasets.
This method was used to generate useful alleles of the roX1 gene, which produces a noncoding RNA involved in dosage compensation.
A possible solution to the problem of generalization is to apply statistical methods that can generate useful robot responses in situations for which the robot has not been specifically instructed how to respond.
By the same token, other methods can also help generate useful data, as well as a greater sense of trust in AI tools.
However, depending on the degree of nonlinearity of the system at hand, these linear methods may not always generate useful results.
The Multivoc method generates multiple VLAD representations by multiple quantizations, and then, the PCA method is performed to filter duplicate parts to obtain additional useful information.
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