Sentence examples for unified datasets from inspiring English sources
Unfortunately, this is the price we have to pay for having unified datasets among wider areas (EU in our case).
For the unified datasets, however, both RF and LDA attained top scores.
Notably, none of the image-derived features were present in the top selected features of the unified datasets, as shown in Tables 1 and 2. In order to assess the consistency of the ranking of image features, 50 repetitions of the random forest algorithm were executed for each of the unified datasets derived by the four imputation methods (m.i, nr.i, u.i, and b.i).
Additionally, in the same linear perspective, the image data points present better class separation in the unified datasets, as denoted by the crossed marks in the PCA and LDA scores plots.
This time, however, for the derivation of the unified datasets we replicated the image features 10 times, so adding 310 replicated image features, in order to balance the feature size effect with the microarray features.
It is worth noting the dramatic improvement of the predictor sets inferred by the unified datasets (although biased), in terms of classification performance as well as informational content regarding the explained variation.
The imputations applied using the nr.i, u.i, and b.i schemes resulted in a balanced selection of the predictor set from the derived, unified dataset each time.
The features from the mean-imputation unified dataset presented higher instability than all other methods and so proved to be the least preferable approach for the imputation procedure.
As shown in the feature of Tables 1 and 2, the normal random imputation dataset (nr.i) resulted in a considerably more stable selection of features compared to the mean imputation unified dataset (m.i).
The output consists of the unified transformed datasets, which finally feed the data partitioning and organization stage (Line 22).
The unified transformed datasets are directly passed to the mappers of stage 2, which also receive the partitioning ranges as a broadcast dataset via the withBroadCastSet operation.
