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The method recognizes a set of machining features called maximal features in a designed part by decomposing the delta volume, which is the difference between the stock and the part.
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Maximal features are intermediate features.
By performing process planning with maximal features, the optimal or satisfactory feature interpretation will be found.
Results for the individual classifiers using the minimal and maximal feature set are summarized in Table 2.
In the maximal feature set, indicators for previous use of a drug and the baseline VL measure extend this list.
In addition to features from the minimal set, the maximal feature set comprises indicators for mutations in previously observed genotypes, the number of past treatment lines, and the VL measure at baseline.
Features that can be derived from the minimal (maximal) feature set during prediction were allowed for usage in the minimal (maximal) feature set as well.
The use of the maximal feature set consistently outperformed the minimal feature set in the combined system.
In fact, the three classifiers agreed on the same label in 80.4% (81.7%) of the cases using the minimal (maximal) feature set.
In the remainder of this paper we will refer to features derived from the minimal and full set of information as minimal feature set and maximal feature set, respectively.
These results were qualitatively the same when using the maximal feature set.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com