Table 3 - uploaded by Paweł Ksieniewicz
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In this work we explored capabilities of improving deep learning models performance by reducing the dataset imbalance. For our experiments a highly imbalanced ECG dataset MIT-BIH was used. Multiple approaches were considered. First we introduced mutliclass UMCE, the ensemble designed to deal with imbalanced datasets. Secondly, we studied the impact...

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... expected, for all models, the most problematic classes were the ones with the lowest number of samples (1 and 3). Based on the results of Kruskal tests presented in Table 3 with α = 0.005 significance level, we can conclude that all algorithms differ in a significant way. We analyse the post-hoc tests results provided in Table 4 with the same significance level. ...

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