Exact(1)
To account for this, data transformations were conducted where appropriate, to stabilize the seasonal variations [ 13].
Similar(59)
This data transformation produces a loss of information with regards to the original dataset which affects the utility of masked data, so, the aim of microaggregation algorithms is to find the partition that minimises the information loss while ensuring a certain level of privacy.
However, we want to note that the main conclusions of the modeling are not dependent on this data transformation.
Because there is no internal standard when using UV-LDI mass spectrometry, this data transformation is necessary in order to carry out multivariate statistical analysis [7].
A reasonable alternative is to transform all cell frequency measurements using the arcsine-square-root [18], and the results we present here are generated using this data transformation.
For parameter estimation the measured areas A for CLV3 and WUS were transformed to volumes V assuming a spherical symmetry of the cell pools:
Therefore, the performance of the predictive algorithm was not affected by this data transformation step.
Recently, however, has been shown that glioma tumor types can be perfectly separated using binarily receded expression vectors [ 53], confirming that this data transformation preserved high information content of expression vectors.
Despite the benefits mentioned, this data transformation introduced a computational limitation due to the enormous amount of feature combinations to be processed, especially when N-feature model classifiers for N>4 are considered (If 100 features are selected, and 10-feature model classifier is investigated, the search space will be C10= 1.731030945644000 × 1013 different combination of features).
In this paper three different approaches were explored: 1. Implement the data transformation logic within the data pipeline.
Implement the data transformation logic within the data pipeline.
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