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The problem transformation methods transform the multi-label classification problem either into one or more single-label classification or regression problems, and there have been many learning algorithms depending on transformation methods.
Several variance-stabilization and normalization transformation methods, which try to transform expression values to be equal variance and normally distributed for each gene, have been proposed [ 19- 23].
Table 1 shows the microbial transformation methods.
RCDTC had the greatest correlation consistently over different transformation methods.
This will be compared to already known transformation methods.
Comparison between the two transformation methods is also reported.
Nonetheless, it works on any data set, independent of the data transformation methods used [31].
This allows the use of inverse Abel transformation methods that enhance the resolution further.
Fig. 7 Correlation coefficient changes with SNR by using different T-F transformation methods.
Applying the above transformation methods, an attribute vector with zero skewness is obtained.
and correlation between No. of conformers and No. of R.C Transformation methods Clustering algo.
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