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Since the levels of MIF differed between primary and metastatic melanoma, analyses were conducted on each classification where tumour biopsies were initially segregated into quartiles of MIF expression ranging from low to high values.
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The data set was split into two groups based on the value of each classification variables to see if there is a statistically significant difference in the value of classification variable between the two groups.
Table 4 summarizes the mean classification accuracy of each classification method on the considered datasets.
Tables 2 and 3 show the average correct result for each classification approach on six database with MFCC and SIFT features.
Using the classifier's outputs as a ranking method and selecting the n-best sentences as candidates, we computed the f-measure scores for each classification algorithm on the Outcome element.
Thus, the choice of the algorithm would depend on each specific classification or domain.
Table 16 shows the ranking of suppliers in each classification based on "extremely preferred", "moderately preferred", and "weakly preferred" categories.
Collaborative filtering is a classification-based technique, i.e. depends on the classification each user gave to the items he/she was exposed to [5].
On each pair, a classification tree was constructed and tested using a computational statistic Matlab toolbox [ 24], where each gene was a node variable and in this way a gene forest with 500 trees was constructed.
On the other hand, wrapper method ranks features based on their effects on classification accuracy.
Based on these classification systems, each individual claim was classified as IRD-related or not-IRD-related for the analysis.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com